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AMD unveils Ryzen Pro 8000-series processors

172 points by thm 3y ago

InTheArena 3y ago
While everyone has focused on Apple's power-efficiency on the M series chips, one thing that has been very interesting is how powerful the unified memory model (by having the memory on-package with CPU) with large bandwidth to the memory actually is. Hence a lot of people in the local LLMA community are really going after high-memory Macs. It's great to see NPUs here with the new Ryzen cores - but I wonder how effective they will be with off-die memory versus the Apple approach. That said, it's nothing but great to see these capabilities in something other then a expensive NVIDIA card. Local NPUs may really help with edge deploying more conferencing capabilities. Edited - sorry, ,meant on-package.
- vbezhenar 3y ago
  Apple does not make on-die RAM.
- chaostheory 3y ago
  What Apple has is theoretically great on paper, but it fails to live up to expectations. Whats the point of having the RAM for running an LLM locally when the performance is abysmal compared to running it on even a consumer Nvidia GPU. It’s a missed opportunity that I hope either the M4 or M5 addresses
  - bearjaws 3y ago
    It's a 25w processor. How will it ever live up to a 400w GPU? Also you can't even run large models on a single 4090, but you can on M series laptops with enough RAM. The fact a laptop can run 70B+ parameter models is a miracle, it's not what the chip was built to do at all.
    - chaostheory 3y ago
      The problem is that it extends to both Mac Studio and Mac Pro.
    - wongarsu 3y ago
      It's a valid comparison in the very limited sense of "I have $2000 to spend on a way to run LLMs, should I get an RTX4090 for the computer I have or should I get a 24GB MacBook", or "I have $5000, should I get an RTX A6000 48GB or a 96GB MacBook". Those comparisons are unreasonable in a sense, but they are implied by statements like GPs "Hence a lot of people in the local LLMA community are really going after high-memory Macs".
      - fckgw 3y ago
        No, it is not a valid comparison to make between an entire laptop and a single PC part. "The computer I have" is doing a ton of heavy lifting here.
        wongarsu 3y ago
        "Should I upgrade what I have or buy something new" is a completely normal everyday decision. Of course it doesn't apply to everyone since it presumes you have something compatible to upgrade, but it is a real decision lots of people are making
        fckgw 3y ago
        But it's also assuming everyone has a desktop PC capable of this stuff that can be upgraded.
        michaelt 3y ago
        Sorta yes, sorta no. You're certainly right that with a macbook you get a whole computer, so you're getting more for your money. And it's a luxury high-end computer too! But personally, I've never seen anyone step directly from not-even-having-a-PC to buying a 4090 for $1800. Folks that aren't technically inclined by and large stick with hosted models like ChatGPT. More common in my experience is for technical folks with, say, an 8GB GPU to experiment with local ML, decide they're interested in it, then step up to a 4090 or something.
  - InTheArena 3y ago
    The performance of oolama on my M1 MAX is pretty solid - and does things that my 2070 GPU can't do because of memory.
    - dangus 3y ago
      Not that I don’t believe you but the 2070 is two generations and 5 years old. Maybe a comparison to a 4000 series would be more appropriate?
      - Kirby64 3y ago
        The M1 Max is also 2 generations old, and ~3 years old at this point. Seems like a fair comparison to me.
        dangus 3y ago
        The 4000 series still has a bigger gap in how much of a generational leap that product was. The M3 Max has something like 33% faster overall graphics performance than the M1 Max (average benchmark) while the 4090 is something like 138% faster than the 2080Ti. Depending on which 2070 and 4070 models you compare the difference is similar, close to or exceeding 100% uplift.
        whizzter 3y ago
        Googling power draw the 4090 goes up to 450w whilst the 2080ti was at 250w, adjusting for power consumption the increase is somewhere around 32%. Some architectural gains and probably optimizations in chipset workings but we're not seeing as many amazing generational leaps anymore regardless of manufacturer/designer.
        dangus 3y ago
        I’m still seeing over a 100% uplift comparing mobile to mobile on Nvidia products: https://gpu.userbenchmark.com/Compare/Nvidia-RTX-4090-Laptop-vs-Nvidia-RTX-2080-Mobile/m2036852vsm700275 https://gpu.userbenchmark.com/Compare/Nvidia-RTX-4090-Laptop... As far as desktop products, power consumption is irrelevant.
  - zitterbewegung 3y ago
    Buying a m3 max with 128gb of RAM while will underperform any consumer NVIDIA GPU it will be able to load larger models in practice but slowly. I think a way for the m series chips to aggressively target GPU inference or training would need a strategy that increases the speed of the RAM to start to match GDDR6 or HBM3 or use it directly.
    - deleted 3y ago
      [deleted]
    - chaostheory 3y ago
      You summed up my point better than I did
  - evilduck 3y ago
    That completely depends on your expectations and uses. I have a gaming rig with a 4080 with 16GB of RAM and it can't even run Mixtral (kind of the minimum bar of a useful generic LLM in my opinion) without being heavily quantized. Yeah it's fast when something fits on it, but I don't see much point in very fast generation of bad output. A refurbished M1 Max with 32GB of RAM will enable you to generate better quality LLM output than even a 4090 with 24GB of VRAM and for ~$300 less, and it's a whole computer instead of a single part that still needs a computer around it. Compared to my 4080, that GPU and the surrounding computer get you half the VRAM capacity for greater cost than the Mac. If you're building a rig with multiple GPUs to support many users or for internal private services and are willing to drop more than $3k then I think the equation swings back in favor of Nvidia, but not until then.
    - soupbowl 3y ago
      Just buy another 16gb of ram for 80$....
      - elzbardico 3y ago
        GPU ram?
      - evilduck 3y ago
        Running your larger-than-your-GPU-VRAM LLM model on regular DDR ram will completely slaughter your token/s speed to the point that the Mac comes out ahead again.
        programd 3y ago
        Depends on what you're doing. Just chatting with the AI? I'm getting about 7 tokens per sec for Mistral with the Q6_K on a bog standard Intel i5-11400 desktop with 32G of memory and no discrete GPU (the CPU has Intel UHD Graphics 730 built in). 2 year old low end CPU that goes for, what $150? these days. As far as I'm concerned that's conversational speed. Pop in some 8 core modern CPU and I'm betting you can double that, without even involving any GPU. People way overestimate what they need in order to play around with models these days. Use llama.cpp and buy that extra $80 worth of RAM and pay about half the price of a comparable Mac all in. Bigger models? Buy more RAM, which is very cheap these days. There's a $487 special on Newegg today with an i7-12700KF, motherboard and 32G of ram. Add another $300 worth of case, power supply, SSD and more RAM and you're under the price of a Macbook Air. There's your LLM inference machine (not for training obviously) which can run even the 70B models at home at acceptable conversational speed.
  - deleted 3y ago
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  - instagib 3y ago
    One thing I would consider is usage throttling on a MacBook Pro. Would repeated LLM usage run into throttling? No idea what specifically everyone is pulling their performance data from or what task(s). Here is a video to help visualize the differences with a maxed out m3 max vs 16gbm1 pro vs 4090 on llm 7B/13b/70b llama 2. https://youtu.be/jaM02mb6JFM https://youtu.be/jaM02mb6JFM Here’s a Reddit comparison of 4090 vs M2 Ultra 96gb with tokens/s https://old.reddit.com/r/LocalLLaMA/comments/14319ra/rtx_40906000_vs_m2_max_with_96gb_unified_memory/ https://old.reddit.com/r/LocalLLaMA/comments/14319ra/rtx_409... M3 pro memory BW 150 gb/s M3 max 10/30 300 gb/s M3 max 12/40 400 gb/s “Llama models are mostly limited by memory bandwidth. rtx 3090 has 935.8 gb/s rtx 4090 has 1008 gb/s m2 ultra has 800 gb/s m2 max has 400 gb/s so 4090 is 10% faster for llama inference than 3090 and more than 2x faster than apple m2 max https://github.com/turboderp/exllama https://github.com/turboderp/exllama using exllama you can get 160 tokens/s in 7b model and 97 tokens/s in 13b model while m2 max has only 40 tokens/s in 7b model and 24 tokens/s in 13b apple 40/s Memory bandwidth cap is also the reason why llamas work so well on cpu (…) buying second gpu will increase memory capacity to 48gb but has no effect on bandwidth so 2x 4090 will have 48gb vram and 1008 gb/s bandwidth and 50% utilization”
  - john_alan 3y ago
    what are you talking about, it's literally the fastest single core retail CPU globally, and multicore is close too - https://browser.geekbench.com/processor-benchmarks https://browser.geekbench.com/processor-benchmarks
    - edward28 3y ago
      I would take those benchmarks with a grain of salt, given that it shows a 96 core epyc loosing in multi thread to a 64 core epyc and a 32 core xeon.
  - jwr 3y ago
    Hmm. I'm running decent LLMs locally (deepseek-coder:33b-instruct-q8_0, mistral:7b-instruct-v0.2-q8_0, mixtral:8x7b-instruct-v0.1-q4_0) on my MacBook Pro and they respond pretty quickly. At least for interactive use they are fine and comparable to Anthropic Opus in speed. That MacBook has an M3 Max and 64GB RAM. I'd say it does live up to my expectations, perhaps even slightly exceeds them.
- atty 3y ago
  Apples memory is on package, not on die.
- thsksbd 3y ago
  Old becomes new, the SGI O2 had (off chip) a unified memory model for performance reasons. Not a CS guy, but it seems to me that NUMA like architecture has to come back. Large RAM on chip (balancing a thermal budget between #ofcores vs ram), a much larger RAM off chip and even more RAM through a fast interconnect on a single kernel image. Like the Origin 300 had.
  - Rinzler89 3y ago
    UMA in the SGI machines (and gaming consoles) made sense because all the memory chips at that time were equally slow, or fast, depending how you wanna look at it. PC HW split the video memory from system memory once GDDRAM become so much faster than system RAM, but GDDRAM has too high latency for CPUs and DDR has too low bandwidth for GPUs, so the separation made sense for each's strengths and still does to this day. Unifying it again, like with AMD's APUs, means either compromises for the CPU or for the GPU. There's no free lunch. Currently AMD APUs on the PC use unified DDRAM so CPU performance is top but GPU/NPU perforce is bottlenecked. If they were to use unified GDDRAM like in the PS5/Xbox then GPU/NPU performance would be top and CPU performance would be bottlenecked.
    - Dalewyn 3y ago
      >Unifying it again, like with AMD's APUs, means either compromises for the CPU or for the GPU. There's no free lunch. I think the lunch here (it still ain't free) is that RAM speed means nothing if you don't have enough RAM in the first place, and this is a compromise solution to that practical problem.
      - Rinzler89 3y ago
        >if you don't have enough RAM in the first place Enough RAM for what task exactly? System RAM is plentiful and cheap nowadays(unless you buy Apple). I got new a laptop with 32GB RAM for about 750 Euros. But the speeds are too low for high-end gamming or LLM training for the poor APU.
        3y ago
- v1sea 3y ago
  edit: I was wrong.
  - deleted 3y ago
    [deleted]
  - oflordal 3y ago
    You can do that on both HIP and cuda through e.g. hipHostMalloc and the cuda equivalent (Not officially supported on the AMD APUs but works in practice). With a discrete GPU the GPU will access memory across PCIe but on an APU it will go full speed to RAM as far as I can tell.
  - smallmancontrov 3y ago
    > low latency results each frame What does that do to your utilization? I've been out of this space for a while, but in game dev any backwards information flow (GPU->CPU) completely murdered performance. "Whatever you do, don't stall the pipeline." Instant 50%-90% performance hit. Even if you had to awkwardly duplicate calculations on the CPU, it was almost always worth it, and not by a small amount. The caveat to "almost" was that if you were willing to wait 2-4 frames to get data back, you could do that without stalling the pipeline. I didn't think this was a memory architecture thing, I thought it was a data dependency thing. If you have to finish all calculations before readback and if you have to readback before starting new calculations, the time for all cores to empty out and fill back up is guaranteed to be dead time, regardless of whether the job was rendering polygons or finite element calculations or neural nets. Does shared memory actually change this somehow? Or does it just make it more convenient to shoot yourself in the foot? EDIT: or is the difference that HPC operates in a regime where long "frame time" dwarfs the pipeline empty/refill "dead time"?
    - v1sea 3y ago
      It was probably from my workloads being relatively small that I could get away with 90Hz read on the cpu side. I'll need to dig deeper into it. The metrics I was seeing were showing 200-300 microseconds of GPU time for physics calculations and within the same frame the cpu reading from that buffer. Maybe I'm wrong, need to test more.
- numpad0 3y ago
  Note that while UMA is great in the sense that they allow LLM models to be run at all, M-series chips aren't faster[1] when the model fits in VRAM. 1: screenshot from[2]: https://www.igorslab.de/wp-content/uploads/2023/06/Apple-M2-ULtra-SoC-Geekbench-5-OpenCL-Compute.jpg 2: https://wccftech.com/apple-m2-ultra-soc-isnt-faster-than-amd-intel-last-year-desktop-cpus-50-slower-than-nvidia-rtx-4080/
  - cstejerean 3y ago
    The problem is you're limited to 24 GB of VRAM unless you pay through the nose for datacenter GPUs, whereas you can get an M-series chip with 128 GB or 192 GB of unified memory.
    - numpad0 3y ago
      Surely! The point is that they're not million times faster magic chips that makes NVIDIA bankrupt tomorrow. That's all. A laptop with up to 128GB "VRAM" is a great option, absolutely no doubt about that.
      - john_alan 3y ago
        They are powerful, but I agree with you, it's nice to be able to run Goliath locally, but it's a lot slower than my 4070.
  - paulmd 3y ago
    that's openCL compute, LLM models ideally should be hitting the neural accelerator, not running on generalized gpu compute shaders.
- spamizbad 3y ago
  My understanding is the unified RAM on the M-series die does not contribute significantly to their performance. You get a little bit better latency but not much. The real value to Apple is likely it greatly simplifies your design since you don't have to route out tons of DRAM signaling and power management on your logic board. Might make DRAM training easier too but that's way beyond my expertise.
  - john_alan 3y ago
    also provides the GPU with serious RAM allocation, a 64GB M3 chip comes with more ram for the GPU than a 4090
  - rowanG077 3y ago
    So why are you proposing Apple did that if not for performance as you claim? They waste a lot of silicon for those extra memory controllers. Basically a comparable amount to all the CPU cores in an M1 Max.
- AceJohnny2 3y ago
  > unified memory model (by having the memory on-package with CPU) That's not what "unified memory model" means. It means that the CPU and GPU (and ANE!) have access to the same banks of memory, unlike PC GPUs that have their own memory, separated from the CPU's by the PCIe bottleneck (as fast as that is, it's still smaller than direct shared DRAM access). It allows the hardware more flexibility in how the single pool of memory is allocated across devices, and faster sharing of data across devices. (throughput/latency depends on the internal system bus ports and how many each device have access to) The Apple M-Series chips also has the memory on-package with the CPU (technically SoC, "System-on-Chip"), but that provides different benefits.
  - cmovq 3y ago
    Having separated GPU memory also has its benefits. Once the data makes it through the PCIe bus, graphics memory typically has much higher bandwidth which also doesn’t need to split with the CPU.
    - crawshaw 3y ago
      An M2 Ultra has 800GB/s of memory bandwidth, an Nvidia 4090 has 1008GB/s. Apple have chosen to use relatively little system memory at unusually high bandwidth.
    - mmaniac 3y ago
      The benefit when having an ecosystem of discrete GPUs is that CPUs can get away with having low bandwidth memory. This is great if you want motherboards with socketed CPU and socketed RAM which are compatible with the whole range of product segments. CPUs don't really care about memory bandwidth until you get to extreme core counts (Threadripper/Xeon territory). Mainstream desktop and laptop CPUs are fine with just two channels of reasonably fast memory. This would bottleneck an iGP, but those are always weak anyway. The PC market has told users who need more to get a discrete GPU and to pay the extra costs involved with high bandwidth soldered memory only if they need it. The calculation Apple has made is different. You'll get exactly what you need as a complete package. You get CPU, GPU, and the bandwidth you need to feed both as a single integrated SoC all the way to the high end. Modularity is something PC users love but doing away with it does have advantages for integration.
Havoc 3y ago
Are these NPUs addressable with a standard PyTorch LLM stack? NPU seems to mean very different things depending on device/vendor
bearjaws 3y ago
The focus on TOPS seems a bit out of line with reality for LLMs. TOPs doesn't matter for LLMs if your memory bandwidth can't keep up. Since it doesn't have quad channel memory mentioned I guess it's still dual channel? Even top of the line DDR5 is around 128GB/s vs a M1 at 400GB/s. At the end of the day, it still seems like AI in consumer chips is chasing a buzzword, what is the killer feature? On mobile there are image processing benefits and voice to text, translation... but on desktop those are no where near common use cases.
- postalrat 3y ago
  https://www.neatvideo.com/blog/post/m3 https://www.neatvideo.com/blog/post/m3 That says M1 is 68.25 GB/s
  - givinguflac 3y ago
    The op were obviously talking about M1 Max.
    - postalrat 3y ago
      How is it obvious? Anyone reading that could assume that any M1 gets that bandwidth.
  - bearjaws 3y ago
    M1 Max sorry, I don't mean to compare a 4 year old tablet processor to the latest generation of laptop CPUs.
  - deleted 3y ago
    [deleted]
- VHRanger 3y ago
  The killer feature is presumably inference at the edge, but I don't see that being used on desktop much at all right now. Especially since most desktop applications people use are web apps. Of the native apps people use that leverage this sort of stuff, almost all are GPU accelerated already (eg. image and video editing AI tools)
phkahler 3y ago
Am I missing something? These look just like the APUs with the addition of "management" and "security" features and without the iGPU. Is that right?
- c0l0 3y ago
  They also support ECC UDIMMs with ECC enabled, which has been the "PRO" series APU killer feature on AM4 for me. The non-"PRO" APUs will run fine with ECC UDIMMs, but cannot make use of the extra parity information (maybe for reasons of market segmentation - I don't know if anyone outside of AMD knows). This is probably less of a concern with DDR5 platforms and their "on-DIE ECC" (which you cannot monitor for Correctable Errors at least, afaik), but it's still gonna matter for me.
  - nwah1 3y ago
    Yes. And it actually does have an iGPU, depending on which model.
    - c0l0 3y ago
      In case you don't need an integrated GPU (that's somewhat powerful/potent), you can go with any other Ryzen AM5 CPU to receive proper ECC-enabled ECC UDIMM support, afaik :)
  - pedrocr 3y ago
    Since in AM5 all CPUs have a basic iGPU, for a home server all the normal CPUs already work fine. The advantage of the APUs is they're on a monolithic die so should have quite a bit lower idle power usage which is important if you have a NAS or other homelab server running 24/7.
kokonoko 3y ago
I hope (but I doubt) that this will be more than a marketing stand to include something "AI" in their product line. Every vendor has their own hardware that is badly supported by tools, and even if it is supported, only a fraction of the available software uses it. In the meantime it takes precious die area and resources.
Aissen 3y ago
A quick search into it shows that this Ryzen AI NPU's support isn't integrated into upstream inference frameworks yet — so right now it's just useless silicon surface you pay for :-/
- Rinzler89 3y ago
  Some AMD laptops haven't even yet enabled the NPU in firmware even on the 7000 series wich are about a year old. Meaning it's still useless. I was kinda bummed out they released the 8000 series after I just bought a laptop with 7000 series, but I think I actually dodged a bullet here since it doesn't look like much of an upgrade and the AI silicone screams of very early first gen product to me, as if they rushed it out the door because everyone else was doing "AI" and they needed to also cash in on the hype, kinda like the first gen RTX cards. I think by the time I'll actually upgrade, the AI/NPU tech would have matured considerably and actually be useful.
  - robocat 3y ago
    Does anyone have any mental heuristics for judging how "useless" a feature is? Over decades I have a growing antipathy towards products with too many features. Especially new versions/models where the vaunted features of the previous version/model seem to never have been used by anyone.
    - Rinzler89 3y ago
      >Does anyone have any mental heuristics for judging how "useless" a feature is? My favorite example is the story I got to live through of the first generations of consumer 64 bit CPUs. When the first AMD Athlon 64 came out, everyone I knew was buying them because they though they were getting something totally future proof by jumping early on the 64 bit bandwagon, in 2003, when nobody yet had 4GB+ of RAM and neither Windows nor any software would see 64bit releases till several years later when Vista came out which everyone avoided and staid on Windows XP 32bit waiting for Windows 7. And by the time RAM sizes over 4GB and 64 bit software became even remotely mainstream, we already had dual- and quad-core CPUs miles ahead of those original 64 bit CPUs which were now obsolete (tech progress back then was wild). So just like how 64bit silicone was a useless feature on consumer CPUs, and like the first GPUs with raytracing, I feel like now we're in the same boat with AI silicone in PCs, no much SW support for them and when it does come, these early chips will be obsolete. It's the price of being an early adopter.
myself248 3y ago
What does "commercial market" mean here? The article says these features were already available in the "consumer" versions of these chips -- were those given away for free or something? What about them was not "commercial"? I'm sure there's some market segmentation thing at work here, but this just sounds like a Hallmark-holiday excuse to rehash an old press-release and pretend it's another revolution all over again.
- dhruvdh 3y ago
  You can't buy these pro variants from Microcenter for example, but you can buy them from pre-built OEM desktops. Mostly meant for enterprise customers who buy in bulk, I think.
- transpute 3y ago
  > the article says these features were already available Actually, the article says they were not available: the Pro series is based on AMD's existing consumer-oriented processor models but comes with additional features Pro (enterprise) CPUs include remote management, memory encryption and other security features: https://www.amd.com/en/ryzen-pro https://www.amd.com/en/ryzen-pro
baarsh 3y ago
How come new series comes with options only up to 8 cores, while 5900X and 5950X were already 12 and 16 cores few years ago?
- Arrath 3y ago
  Better sustained performance with the thermal envelope afforded to only 8 cores vs 12+?
- wmf 3y ago
  Desktop Ryzen = up to 16 cores Laptop Ryzen = up to 8 cores (the 8000G are laptop CPUs in a desktop socket)
  - protastus 3y ago
    Dragon Range (e.g., 7945HX) is a laptop/mobile workstation Ryzen with up to 16 cores, but power inefficient compared to the 8000-series due to the chiplet design. Already in market, mostly in gaming laptops.
JonChesterfield 3y ago
Zen4 is very pretty, see https://news.ycombinator.com/item?id=32983406 https://news.ycombinator.com/item?id=32983406. I like their APUs a lot. Using a 4800U in the cable tray under the desk to drive screens on which I'm writing this. One in a laptop for whenever I'm away from the desk. If you're sufficiently determined the compute units on these things are totally usable for running arbitrary code. As in a program that spawns a bunch of threads to work stuff out could have some of those "threads" running on the GPU cores. I wouldn't say the software stack is totally there for out of the box convenience. As in you'll be writing in freestanding ~C and maybe a bit of assembly. I got partway through implementing that and got sidetracked. The GPU libc in LLVM is roughly the production version of some of that hacking. Between these machines coming out and the MI300A landing I really should put something up on github which looks like a pthread_create that executes on the GPU instead.

172 points by thm 3y ago

While everyone has focused on Apple's power-efficiency on the M series chips, one thing that has been very interesting is how powerful the unified memory model (by having the memory on-package with CPU) with large bandwidth to the memory actually is. Hence a lot of people in the local LLMA community are really going after high-memory Macs. It's great to see NPUs here with the new Ryzen cores - but I wonder how effective they will be with off-die memory versus the Apple approach. That said, it's nothing but great to see these capabilities in something other then a expensive NVIDIA card. Local NPUs may really help with edge deploying more conferencing capabilities. Edited - sorry, ,meant on-package.

vbezhenar 3y ago
Apple does not make on-die RAM.
chaostheory 3y ago
What Apple has is theoretically great on paper, but it fails to live up to expectations. Whats the point of having the RAM for running an LLM locally when the performance is abysmal compared to running it on even a consumer Nvidia GPU. It’s a missed opportunity that I hope either the M4 or M5 addresses
- bearjaws 3y ago
  It's a 25w processor. How will it ever live up to a 400w GPU? Also you can't even run large models on a single 4090, but you can on M series laptops with enough RAM. The fact a laptop can run 70B+ parameter models is a miracle, it's not what the chip was built to do at all.
  - chaostheory 3y ago
    The problem is that it extends to both Mac Studio and Mac Pro.
  - wongarsu 3y ago
    It's a valid comparison in the very limited sense of "I have $2000 to spend on a way to run LLMs, should I get an RTX4090 for the computer I have or should I get a 24GB MacBook", or "I have $5000, should I get an RTX A6000 48GB or a 96GB MacBook". Those comparisons are unreasonable in a sense, but they are implied by statements like GPs "Hence a lot of people in the local LLMA community are really going after high-memory Macs".
    - fckgw 3y ago
      No, it is not a valid comparison to make between an entire laptop and a single PC part. "The computer I have" is doing a ton of heavy lifting here.
      - wongarsu 3y ago
        "Should I upgrade what I have or buy something new" is a completely normal everyday decision. Of course it doesn't apply to everyone since it presumes you have something compatible to upgrade, but it is a real decision lots of people are making
        fckgw 3y ago
        But it's also assuming everyone has a desktop PC capable of this stuff that can be upgraded.
      - michaelt 3y ago
        Sorta yes, sorta no. You're certainly right that with a macbook you get a whole computer, so you're getting more for your money. And it's a luxury high-end computer too! But personally, I've never seen anyone step directly from not-even-having-a-PC to buying a 4090 for $1800. Folks that aren't technically inclined by and large stick with hosted models like ChatGPT. More common in my experience is for technical folks with, say, an 8GB GPU to experiment with local ML, decide they're interested in it, then step up to a 4090 or something.
- InTheArena 3y ago
  The performance of oolama on my M1 MAX is pretty solid - and does things that my 2070 GPU can't do because of memory.
  - dangus 3y ago
    Not that I don’t believe you but the 2070 is two generations and 5 years old. Maybe a comparison to a 4000 series would be more appropriate?
    - Kirby64 3y ago
      The M1 Max is also 2 generations old, and ~3 years old at this point. Seems like a fair comparison to me.
      - dangus 3y ago
        The 4000 series still has a bigger gap in how much of a generational leap that product was. The M3 Max has something like 33% faster overall graphics performance than the M1 Max (average benchmark) while the 4090 is something like 138% faster than the 2080Ti. Depending on which 2070 and 4070 models you compare the difference is similar, close to or exceeding 100% uplift.
        whizzter 3y ago
        Googling power draw the 4090 goes up to 450w whilst the 2080ti was at 250w, adjusting for power consumption the increase is somewhere around 32%. Some architectural gains and probably optimizations in chipset workings but we're not seeing as many amazing generational leaps anymore regardless of manufacturer/designer.
        dangus 3y ago
        I’m still seeing over a 100% uplift comparing mobile to mobile on Nvidia products: https://gpu.userbenchmark.com/Compare/Nvidia-RTX-4090-Laptop-vs-Nvidia-RTX-2080-Mobile/m2036852vsm700275 https://gpu.userbenchmark.com/Compare/Nvidia-RTX-4090-Laptop... As far as desktop products, power consumption is irrelevant.
- zitterbewegung 3y ago
  Buying a m3 max with 128gb of RAM while will underperform any consumer NVIDIA GPU it will be able to load larger models in practice but slowly. I think a way for the m series chips to aggressively target GPU inference or training would need a strategy that increases the speed of the RAM to start to match GDDR6 or HBM3 or use it directly.
  - deleted 3y ago
    [deleted]
  - chaostheory 3y ago
    You summed up my point better than I did
- evilduck 3y ago
  That completely depends on your expectations and uses. I have a gaming rig with a 4080 with 16GB of RAM and it can't even run Mixtral (kind of the minimum bar of a useful generic LLM in my opinion) without being heavily quantized. Yeah it's fast when something fits on it, but I don't see much point in very fast generation of bad output. A refurbished M1 Max with 32GB of RAM will enable you to generate better quality LLM output than even a 4090 with 24GB of VRAM and for ~$300 less, and it's a whole computer instead of a single part that still needs a computer around it. Compared to my 4080, that GPU and the surrounding computer get you half the VRAM capacity for greater cost than the Mac. If you're building a rig with multiple GPUs to support many users or for internal private services and are willing to drop more than $3k then I think the equation swings back in favor of Nvidia, but not until then.
  - soupbowl 3y ago
    Just buy another 16gb of ram for 80$....
    - elzbardico 3y ago
      GPU ram?
    - evilduck 3y ago
      Running your larger-than-your-GPU-VRAM LLM model on regular DDR ram will completely slaughter your token/s speed to the point that the Mac comes out ahead again.
      - programd 3y ago
        Depends on what you're doing. Just chatting with the AI? I'm getting about 7 tokens per sec for Mistral with the Q6_K on a bog standard Intel i5-11400 desktop with 32G of memory and no discrete GPU (the CPU has Intel UHD Graphics 730 built in). 2 year old low end CPU that goes for, what $150? these days. As far as I'm concerned that's conversational speed. Pop in some 8 core modern CPU and I'm betting you can double that, without even involving any GPU. People way overestimate what they need in order to play around with models these days. Use llama.cpp and buy that extra $80 worth of RAM and pay about half the price of a comparable Mac all in. Bigger models? Buy more RAM, which is very cheap these days. There's a $487 special on Newegg today with an i7-12700KF, motherboard and 32G of ram. Add another $300 worth of case, power supply, SSD and more RAM and you're under the price of a Macbook Air. There's your LLM inference machine (not for training obviously) which can run even the 70B models at home at acceptable conversational speed.
- deleted 3y ago
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- instagib 3y ago
  One thing I would consider is usage throttling on a MacBook Pro. Would repeated LLM usage run into throttling? No idea what specifically everyone is pulling their performance data from or what task(s). Here is a video to help visualize the differences with a maxed out m3 max vs 16gbm1 pro vs 4090 on llm 7B/13b/70b llama 2. https://youtu.be/jaM02mb6JFM https://youtu.be/jaM02mb6JFM Here’s a Reddit comparison of 4090 vs M2 Ultra 96gb with tokens/s https://old.reddit.com/r/LocalLLaMA/comments/14319ra/rtx_40906000_vs_m2_max_with_96gb_unified_memory/ https://old.reddit.com/r/LocalLLaMA/comments/14319ra/rtx_409... M3 pro memory BW 150 gb/s M3 max 10/30 300 gb/s M3 max 12/40 400 gb/s “Llama models are mostly limited by memory bandwidth. rtx 3090 has 935.8 gb/s rtx 4090 has 1008 gb/s m2 ultra has 800 gb/s m2 max has 400 gb/s so 4090 is 10% faster for llama inference than 3090 and more than 2x faster than apple m2 max https://github.com/turboderp/exllama https://github.com/turboderp/exllama using exllama you can get 160 tokens/s in 7b model and 97 tokens/s in 13b model while m2 max has only 40 tokens/s in 7b model and 24 tokens/s in 13b apple 40/s Memory bandwidth cap is also the reason why llamas work so well on cpu (…) buying second gpu will increase memory capacity to 48gb but has no effect on bandwidth so 2x 4090 will have 48gb vram and 1008 gb/s bandwidth and 50% utilization”
- john_alan 3y ago
  what are you talking about, it's literally the fastest single core retail CPU globally, and multicore is close too - https://browser.geekbench.com/processor-benchmarks https://browser.geekbench.com/processor-benchmarks
  - edward28 3y ago
    I would take those benchmarks with a grain of salt, given that it shows a 96 core epyc loosing in multi thread to a 64 core epyc and a 32 core xeon.
- jwr 3y ago
  Hmm. I'm running decent LLMs locally (deepseek-coder:33b-instruct-q8_0, mistral:7b-instruct-v0.2-q8_0, mixtral:8x7b-instruct-v0.1-q4_0) on my MacBook Pro and they respond pretty quickly. At least for interactive use they are fine and comparable to Anthropic Opus in speed. That MacBook has an M3 Max and 64GB RAM. I'd say it does live up to my expectations, perhaps even slightly exceeds them.
atty 3y ago
Apples memory is on package, not on die.
thsksbd 3y ago
Old becomes new, the SGI O2 had (off chip) a unified memory model for performance reasons. Not a CS guy, but it seems to me that NUMA like architecture has to come back. Large RAM on chip (balancing a thermal budget between #ofcores vs ram), a much larger RAM off chip and even more RAM through a fast interconnect on a single kernel image. Like the Origin 300 had.
- Rinzler89 3y ago
  UMA in the SGI machines (and gaming consoles) made sense because all the memory chips at that time were equally slow, or fast, depending how you wanna look at it. PC HW split the video memory from system memory once GDDRAM become so much faster than system RAM, but GDDRAM has too high latency for CPUs and DDR has too low bandwidth for GPUs, so the separation made sense for each's strengths and still does to this day. Unifying it again, like with AMD's APUs, means either compromises for the CPU or for the GPU. There's no free lunch. Currently AMD APUs on the PC use unified DDRAM so CPU performance is top but GPU/NPU perforce is bottlenecked. If they were to use unified GDDRAM like in the PS5/Xbox then GPU/NPU performance would be top and CPU performance would be bottlenecked.
  - Dalewyn 3y ago
    >Unifying it again, like with AMD's APUs, means either compromises for the CPU or for the GPU. There's no free lunch. I think the lunch here (it still ain't free) is that RAM speed means nothing if you don't have enough RAM in the first place, and this is a compromise solution to that practical problem.
    - Rinzler89 3y ago
      >if you don't have enough RAM in the first place Enough RAM for what task exactly? System RAM is plentiful and cheap nowadays(unless you buy Apple). I got new a laptop with 32GB RAM for about 750 Euros. But the speeds are too low for high-end gamming or LLM training for the poor APU.
      - 3y ago
v1sea 3y ago
edit: I was wrong.
- deleted 3y ago
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- oflordal 3y ago
  You can do that on both HIP and cuda through e.g. hipHostMalloc and the cuda equivalent (Not officially supported on the AMD APUs but works in practice). With a discrete GPU the GPU will access memory across PCIe but on an APU it will go full speed to RAM as far as I can tell.
- smallmancontrov 3y ago
  > low latency results each frame What does that do to your utilization? I've been out of this space for a while, but in game dev any backwards information flow (GPU->CPU) completely murdered performance. "Whatever you do, don't stall the pipeline." Instant 50%-90% performance hit. Even if you had to awkwardly duplicate calculations on the CPU, it was almost always worth it, and not by a small amount. The caveat to "almost" was that if you were willing to wait 2-4 frames to get data back, you could do that without stalling the pipeline. I didn't think this was a memory architecture thing, I thought it was a data dependency thing. If you have to finish all calculations before readback and if you have to readback before starting new calculations, the time for all cores to empty out and fill back up is guaranteed to be dead time, regardless of whether the job was rendering polygons or finite element calculations or neural nets. Does shared memory actually change this somehow? Or does it just make it more convenient to shoot yourself in the foot? EDIT: or is the difference that HPC operates in a regime where long "frame time" dwarfs the pipeline empty/refill "dead time"?
  - v1sea 3y ago
    It was probably from my workloads being relatively small that I could get away with 90Hz read on the cpu side. I'll need to dig deeper into it. The metrics I was seeing were showing 200-300 microseconds of GPU time for physics calculations and within the same frame the cpu reading from that buffer. Maybe I'm wrong, need to test more.
numpad0 3y ago
Note that while UMA is great in the sense that they allow LLM models to be run at all, M-series chips aren't faster[1] when the model fits in VRAM. 1: screenshot from[2]: https://www.igorslab.de/wp-content/uploads/2023/06/Apple-M2-ULtra-SoC-Geekbench-5-OpenCL-Compute.jpg 2: https://wccftech.com/apple-m2-ultra-soc-isnt-faster-than-amd-intel-last-year-desktop-cpus-50-slower-than-nvidia-rtx-4080/
- cstejerean 3y ago
  The problem is you're limited to 24 GB of VRAM unless you pay through the nose for datacenter GPUs, whereas you can get an M-series chip with 128 GB or 192 GB of unified memory.
  - numpad0 3y ago
    Surely! The point is that they're not million times faster magic chips that makes NVIDIA bankrupt tomorrow. That's all. A laptop with up to 128GB "VRAM" is a great option, absolutely no doubt about that.
    - john_alan 3y ago
      They are powerful, but I agree with you, it's nice to be able to run Goliath locally, but it's a lot slower than my 4070.
- paulmd 3y ago
  that's openCL compute, LLM models ideally should be hitting the neural accelerator, not running on generalized gpu compute shaders.
spamizbad 3y ago
My understanding is the unified RAM on the M-series die does not contribute significantly to their performance. You get a little bit better latency but not much. The real value to Apple is likely it greatly simplifies your design since you don't have to route out tons of DRAM signaling and power management on your logic board. Might make DRAM training easier too but that's way beyond my expertise.
- john_alan 3y ago
  also provides the GPU with serious RAM allocation, a 64GB M3 chip comes with more ram for the GPU than a 4090
- rowanG077 3y ago
  So why are you proposing Apple did that if not for performance as you claim? They waste a lot of silicon for those extra memory controllers. Basically a comparable amount to all the CPU cores in an M1 Max.
AceJohnny2 3y ago
> unified memory model (by having the memory on-package with CPU) That's not what "unified memory model" means. It means that the CPU and GPU (and ANE!) have access to the same banks of memory, unlike PC GPUs that have their own memory, separated from the CPU's by the PCIe bottleneck (as fast as that is, it's still smaller than direct shared DRAM access). It allows the hardware more flexibility in how the single pool of memory is allocated across devices, and faster sharing of data across devices. (throughput/latency depends on the internal system bus ports and how many each device have access to) The Apple M-Series chips also has the memory on-package with the CPU (technically SoC, "System-on-Chip"), but that provides different benefits.
- cmovq 3y ago
  Having separated GPU memory also has its benefits. Once the data makes it through the PCIe bus, graphics memory typically has much higher bandwidth which also doesn’t need to split with the CPU.
  - crawshaw 3y ago
    An M2 Ultra has 800GB/s of memory bandwidth, an Nvidia 4090 has 1008GB/s. Apple have chosen to use relatively little system memory at unusually high bandwidth.
  - mmaniac 3y ago
    The benefit when having an ecosystem of discrete GPUs is that CPUs can get away with having low bandwidth memory. This is great if you want motherboards with socketed CPU and socketed RAM which are compatible with the whole range of product segments. CPUs don't really care about memory bandwidth until you get to extreme core counts (Threadripper/Xeon territory). Mainstream desktop and laptop CPUs are fine with just two channels of reasonably fast memory. This would bottleneck an iGP, but those are always weak anyway. The PC market has told users who need more to get a discrete GPU and to pay the extra costs involved with high bandwidth soldered memory only if they need it. The calculation Apple has made is different. You'll get exactly what you need as a complete package. You get CPU, GPU, and the bandwidth you need to feed both as a single integrated SoC all the way to the high end. Modularity is something PC users love but doing away with it does have advantages for integration.

Havoc 3y ago

Are these NPUs addressable with a standard PyTorch LLM stack? NPU seems to mean very different things depending on device/vendor

bearjaws 3y ago

The focus on TOPS seems a bit out of line with reality for LLMs. TOPs doesn't matter for LLMs if your memory bandwidth can't keep up. Since it doesn't have quad channel memory mentioned I guess it's still dual channel? Even top of the line DDR5 is around 128GB/s vs a M1 at 400GB/s. At the end of the day, it still seems like AI in consumer chips is chasing a buzzword, what is the killer feature? On mobile there are image processing benefits and voice to text, translation... but on desktop those are no where near common use cases.

postalrat 3y ago
https://www.neatvideo.com/blog/post/m3 https://www.neatvideo.com/blog/post/m3 That says M1 is 68.25 GB/s
- givinguflac 3y ago
  The op were obviously talking about M1 Max.
  - postalrat 3y ago
    How is it obvious? Anyone reading that could assume that any M1 gets that bandwidth.
- bearjaws 3y ago
  M1 Max sorry, I don't mean to compare a 4 year old tablet processor to the latest generation of laptop CPUs.
- deleted 3y ago
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VHRanger 3y ago
The killer feature is presumably inference at the edge, but I don't see that being used on desktop much at all right now. Especially since most desktop applications people use are web apps. Of the native apps people use that leverage this sort of stuff, almost all are GPU accelerated already (eg. image and video editing AI tools)

phkahler 3y ago

Am I missing something? These look just like the APUs with the addition of "management" and "security" features and without the iGPU. Is that right?

c0l0 3y ago
They also support ECC UDIMMs with ECC enabled, which has been the "PRO" series APU killer feature on AM4 for me. The non-"PRO" APUs will run fine with ECC UDIMMs, but cannot make use of the extra parity information (maybe for reasons of market segmentation - I don't know if anyone outside of AMD knows). This is probably less of a concern with DDR5 platforms and their "on-DIE ECC" (which you cannot monitor for Correctable Errors at least, afaik), but it's still gonna matter for me.
- nwah1 3y ago
  Yes. And it actually does have an iGPU, depending on which model.
  - c0l0 3y ago
    In case you don't need an integrated GPU (that's somewhat powerful/potent), you can go with any other Ryzen AM5 CPU to receive proper ECC-enabled ECC UDIMM support, afaik :)
- pedrocr 3y ago
  Since in AM5 all CPUs have a basic iGPU, for a home server all the normal CPUs already work fine. The advantage of the APUs is they're on a monolithic die so should have quite a bit lower idle power usage which is important if you have a NAS or other homelab server running 24/7.

kokonoko 3y ago

I hope (but I doubt) that this will be more than a marketing stand to include something "AI" in their product line. Every vendor has their own hardware that is badly supported by tools, and even if it is supported, only a fraction of the available software uses it. In the meantime it takes precious die area and resources.

Aissen 3y ago

A quick search into it shows that this Ryzen AI NPU's support isn't integrated into upstream inference frameworks yet — so right now it's just useless silicon surface you pay for :-/

Rinzler89 3y ago
Some AMD laptops haven't even yet enabled the NPU in firmware even on the 7000 series wich are about a year old. Meaning it's still useless. I was kinda bummed out they released the 8000 series after I just bought a laptop with 7000 series, but I think I actually dodged a bullet here since it doesn't look like much of an upgrade and the AI silicone screams of very early first gen product to me, as if they rushed it out the door because everyone else was doing "AI" and they needed to also cash in on the hype, kinda like the first gen RTX cards. I think by the time I'll actually upgrade, the AI/NPU tech would have matured considerably and actually be useful.
- robocat 3y ago
  Does anyone have any mental heuristics for judging how "useless" a feature is? Over decades I have a growing antipathy towards products with too many features. Especially new versions/models where the vaunted features of the previous version/model seem to never have been used by anyone.
  - Rinzler89 3y ago
    >Does anyone have any mental heuristics for judging how "useless" a feature is? My favorite example is the story I got to live through of the first generations of consumer 64 bit CPUs. When the first AMD Athlon 64 came out, everyone I knew was buying them because they though they were getting something totally future proof by jumping early on the 64 bit bandwagon, in 2003, when nobody yet had 4GB+ of RAM and neither Windows nor any software would see 64bit releases till several years later when Vista came out which everyone avoided and staid on Windows XP 32bit waiting for Windows 7. And by the time RAM sizes over 4GB and 64 bit software became even remotely mainstream, we already had dual- and quad-core CPUs miles ahead of those original 64 bit CPUs which were now obsolete (tech progress back then was wild). So just like how 64bit silicone was a useless feature on consumer CPUs, and like the first GPUs with raytracing, I feel like now we're in the same boat with AI silicone in PCs, no much SW support for them and when it does come, these early chips will be obsolete. It's the price of being an early adopter.

myself248 3y ago

What does "commercial market" mean here? The article says these features were already available in the "consumer" versions of these chips -- were those given away for free or something? What about them was not "commercial"? I'm sure there's some market segmentation thing at work here, but this just sounds like a Hallmark-holiday excuse to rehash an old press-release and pretend it's another revolution all over again.

dhruvdh 3y ago
You can't buy these pro variants from Microcenter for example, but you can buy them from pre-built OEM desktops. Mostly meant for enterprise customers who buy in bulk, I think.
transpute 3y ago
> the article says these features were already available Actually, the article says they were not available: the Pro series is based on AMD's existing consumer-oriented processor models but comes with additional features Pro (enterprise) CPUs include remote management, memory encryption and other security features: https://www.amd.com/en/ryzen-pro https://www.amd.com/en/ryzen-pro

baarsh 3y ago

How come new series comes with options only up to 8 cores, while 5900X and 5950X were already 12 and 16 cores few years ago?

Arrath 3y ago
Better sustained performance with the thermal envelope afforded to only 8 cores vs 12+?
wmf 3y ago
Desktop Ryzen = up to 16 cores Laptop Ryzen = up to 8 cores (the 8000G are laptop CPUs in a desktop socket)
- protastus 3y ago
  Dragon Range (e.g., 7945HX) is a laptop/mobile workstation Ryzen with up to 16 cores, but power inefficient compared to the 8000-series due to the chiplet design. Already in market, mostly in gaming laptops.

JonChesterfield 3y ago

Zen4 is very pretty, see https://news.ycombinator.com/item?id=32983406 https://news.ycombinator.com/item?id=32983406. I like their APUs a lot. Using a 4800U in the cable tray under the desk to drive screens on which I'm writing this. One in a laptop for whenever I'm away from the desk. If you're sufficiently determined the compute units on these things are totally usable for running arbitrary code. As in a program that spawns a bunch of threads to work stuff out could have some of those "threads" running on the GPU cores. I wouldn't say the software stack is totally there for out of the box convenience. As in you'll be writing in freestanding ~C and maybe a bit of assembly. I got partway through implementing that and got sidetracked. The GPU libc in LLVM is roughly the production version of some of that hacking. Between these machines coming out and the MI300A landing I really should put something up on github which looks like a pthread_create that executes on the GPU instead.