6 ms·
Flash-MoE: Running a 397B Parameter Model on a Laptop
- homarp 7mo ago/r/localllama discussion: https://old.reddit.com/r/LocalLLaMA/comments/1rxmmu5/running_qwen35_397b_on_m3_macbook_pro_with_48gb/ https://old.reddit.com/r/LocalLLaMA/comments/1rxmmu5/running...
- harshhhhhhhhh 7mo agoseems promising , this is the way , can someone benchmark this
- frwickst 7mo agoI'm getting 6.55t/s using the Qwen3.5-397B-A17B-4bit model with the command: ./infer --prompt "Explain quantum computing" --tokens 100 MacBook Pro M5 Pro (64GB RAM)
- logicallee 7mo agocan you post the final result (or as far as you got before you killed it) to show us how cohesive and good it is? I'd like to see an example of the output of this.
- frwickst 7mo agoSince the output is quite long, here is a link: https://pastebin.com/k76wiVGP https://pastebin.com/k76wiVGP
- hrimfaxi 7mo agoWhy does this G character appear to prefix most of the output? ("Ġlike")
- kgeist 7mo agoThe original tokens have Ġ instead of space. I had this issue too when writing an inference engine for Qwen. You have to "normalize" those special characters.
- frwickst 7mo agoIt is a tokenizer artifact most likely (https://github.com/huggingface/transformers/issues/4786 https://github.com/huggingface/transformers/issues/4786). So the output is not properly decoded in this case, it should just be a space.
- j45 7mo agoAppreciate the data point. M5 Max would also be interesting to see once available in desktop form.
- rvz 7mo agoThe technical write up is great, but Mac users should not get too excited just yet on running 300B+ parameter models locally as the TPS isn't that good. >...at 4.4+ tokens/second That is even when it is using 4-bit quantization and it is still at that speed. > The entire 209GB model streams from SSD through a custom Metal compute pipeline. This is my main problem. If I were to run this on a Mac SSD, 24/7 for heavy usage such as Openclaw, that is going to significantly reduce the lifetime of the SSD. Can't imagine using this in the long term right now, but improvements will follow. Still a great write up anyways.
- Roxxik 7mo agoDoes an SSD meaningfully degrade by read only workloads?
- etiam 7mo ago> If I were to run this on a Mac SSD, 24/7 for heavy usage such as Openclaw, that is going to significantly reduce the lifetime of the SSD. How sure are you about that? I've never looked closer at how a large LLM with mixture of experts architecture switches between expert modules, but staying on roughly the same topic for the use (as it often would when editing the same codebase), I wouldn't be surprised to see the switches of composition are fairly rare, fairly small, and to the extent it happens it's repeated reads from the flash disk rather than writes it tends to cause.
- frotaur 7mo ago
- JSR_FDED 7mo agoThis is a very impressive result. If I understand correctly the bottleneck is the SSD in this architecture - the author seems to get almost 15GB/s - but I seem to remember the max b/w was about 8GB/s. What am I missing?
- rado 7mo agoMacBook Pro M5 Pro and M5 Max have such SSD speed
- selimthegrim 7mo agoI have an MBP M4 Pro and a WD Black SN850x in an external TB5 enclosure and I easily get 6-7 GB/s
- Roxxik 7mo agoIO is very bursty in these setups. When the router results are in you can start loading experts from SSD. In this brief moment the SSD is saturated. Outside of that the SSD is idling. Table 3 shows for K=4 experts an IO of 943 MB/Tok at 3.15 Tok/s giving an average IO of 2970 MB/s far below what the SSD could do. I'm not sure, but not all expert weights are used immediately. Maybe they could do async reads for the down tensors parallelizing compute with IO. Not sure if this works on Mac, I only tested my larger than RAM setup on Linux with io_uring O_DIRECT reads and I saw that about 20% of total reads do finish while my fused upgate matmul is already running. Edit: Typos
- zozbot234 7mo agoThe github page mentions that you can't overlap SSD traffic and GPU compute on Apple Silicon, you get heavy contention for the shared hardware resources.
- devnotes77 7mo ago[dead]
- 7mo ago
- bertili 7mo agoVery impressive! I wonder if there is a similar path for Linux using system memory instead of SSD? Hell, maybe even a case for the return of some kind of ROMs of weights?
- K0balt 7mo agoMy thoughts exactly. Something like this could make it so that modest GPU capacity, like a pair of 3090s , and lots of RAM could make big inference more practical for personal labs
- zozbot234 7mo agoLoading experts to system memory is supported by most local-AI frameworks. But you do not gain much by running that part of the decode on GPU, since decode is not compute-limited and the CPU-GPU transfer involves overhead. It's best to use the GPU for speeding up the shared part of the model.
- daemonologist 7mo agoMost definitely - the popular engines have extensive support for doing this and controlling exactly which weights end up where (llama.cpp: https://github.com/ggml-org/llama.cpp/blob/master/tools/cli/README.md https://github.com/ggml-org/llama.cpp/blob/master/tools/cli/... , vllm: https://docs.vllm.ai/en/stable/configuration/engine_args/#offloadconfig https://docs.vllm.ai/en/stable/configuration/engine_args/#of... , sglang (haven't tried this): https://docs.sglang.io/advanced_features/server_arguments.html#offloading https://docs.sglang.io/advanced_features/server_arguments.ht...). Even with a MoE model, which has to move a relatively small portion of the weights around, you do end up quite bandwidth constrained though.
- Aurornis 7mo agoUsing system memory and CPU compute for some of the layers that don’t fit into GPU memory is already supported by common tools. It’s workable for mixture of experts models but the performance falls off a cliff as soon as the model overflows out of the GPU and into system RAM. There is another performance cliff when the model has to be fetched from disk on every pass.
- pdyc 7mo agoimpressive, i wish someone takes a stab at using this technique on mobile gpu's even if it does not use storage it would still be a win. I am running llama.cpp on adreno 830 with oepncl and i am getting pathetic 2-3t/s for output tokens
- vilequeef 7mo agoWhy so much RAM?
- vilequeef 7mo agoOh Mac, unified. Sometimes it takes a downvote
- zozbot234 7mo agoThe github page mentions that a naïve mmap approach is bottlenecked by per-page overhead. Can this be mitigated by setting up explicit "huge" pages? (2M using the CONT PTE feature if the "native" page size is 16k; 32M using a PMD level block mapping; or 1G using the CONT PMD feature.) Does macOS support this out of the box? Alternatively, one might use a simple mmap and then something like posix_fadvise to set up prefetching of the data.
- mugivarra69 7mo ago[dead]
- lostmsu 7mo agoHow large is the KV cache?
- xbar 7mo ago0.1 GB per full-attention layer and "The model has 60 transformer layers: 45 GatedDeltaNet (linear attention) + 15 standard full attention." So, 1.5 GB.
- spwa4 7mo agoDoes this mean that it should be possible to load up a system with ~10 (seems to me at least the number of active experts) SSDs to get 40 tok/s even on truly gigantic models?
- zozbot234 7mo agoSSD bandwidth will ultimately be limited by the amount of PCIe lanes you have available (for something other than the Apple Silicon internal storage). So the approach has inherent limitations. You can of course scale out to multiple systems to get more throughput. You can use this approach with Intel Optane, which is wearout-resistant unlike NAND and can thus substitute for RAM. Last I checked, it was available quite cheap on the secondary market, ~$1/GB as opposed to ~$15/GB or more for DRAM. (Of course that's nowhere near as cheap as NAND, which is around ~$0.1/GB but quite wearout-prone with heavy writes.)
- spwa4 7mo agoYeah, PCIe is the bottleneck. The point being that whether the data originates from RAM or from NVME or Optane, you cannot get data to the GPU faster with RAM than with SSDs. Meanwhile PCIe switches exist. So why not build: 1 CPU + memory + ... N PCIe switch with each 1 low-memory GPU + 6 NVME drives (in theory 5 can saturate the GPU) Each of those should only bother the CPU when they have some tokens produced and have plenty of PCIe lanes to get at their data. Such a setup should be able to get a 6 to 8 times speedup from the solution detailed here, and a model compute increase should make relatively little difference in performance.
- kelnos 7mo agoI'm not sure how you're going to get 10 SSDs to fit inside a laptop. And if you're not going to use a laptop, then you might as well get an expensive machine with plenty of RAM, no? Even with RAM prices as crazy as they are these days, that's probably still cheaper than a machine with enough PCIe bandwidth/lanes/ports for 10 SSDs, not to mention the cost of the SSDs themselves. Though I guess this could be an interesting area of research, regardless of the cost.
- tarruda 7mo agoNote that this is not the only way to run Qwen 3.5 397B on consumer devices, there are excellent ~2.5 BPW quants available that make it viable for 128G devices. I've had great success (~20 t/s) running it on a M1 Ultra with room for 256k context. Here are some lm-evaluation-harness results I ran against it: mmlu: 87.86% gpqa diamond: 82.32% gsm8k: 86.43% ifeval: 75.90% More details of my experience: - https://huggingface.co/ubergarm/Qwen3.5-397B-A17B-GGUF/discussions/8 https://huggingface.co/ubergarm/Qwen3.5-397B-A17B-GGUF/discu... - https://huggingface.co/ubergarm/Qwen3.5-397B-A17B-GGUF/discussions/2 https://huggingface.co/ubergarm/Qwen3.5-397B-A17B-GGUF/discu... - https://gist.github.com/simonw/67c754bbc0bc609a6caedee16fef89e8?permalink_comment_id=5991165#gistcomment-5991165 https://gist.github.com/simonw/67c754bbc0bc609a6caedee16fef8... Overall an excellent model to have for offline inference.
- Aurornis 7mo agoThe method in this link is already using a 2-bit quant. They also reduced the number of experts per token from 10 to 4 which is another layer of quality degradation. In my experience the 2-bit quants can produce output to short prompts that makes sense but they aren’t useful for doing work with longer sessions. This project couldn’t even get useful JSON out of the model because it can’t produce the right token for quotes: > *2-bit quantization produces \name\ instead of "name" in JSON output, making tool calling unreliable.
- tarruda 7mo agoI can't say anything about the OP method, but I already tested the smol-IQ2_XS quant (which has 2.46 BPW) with the pi harness. I did not do a very long session because token generation and prompt processing gets very slow, but I think I worked for up to ~70k context and it maintained a lot of coherence in the session. IIRC the GPQA diamond is supposed to exercise long chains of thought and it scored exceptionally well with 82% (the original BF16 official number is 88%: https://huggingface.co/Qwen/Qwen3.5-397B-A17B https://huggingface.co/Qwen/Qwen3.5-397B-A17B). Note that not all quants are the same at a certain BPW. The smol-IQ2_XS quant I linked is pretty dynamic, with some tensors having q8_0 type, some q6_k and some q4_k (while the majority is iq2_xs). In my testing, this smol-IQ2_XS quant is the best available at this BPW range. Eventually I might try a more practical eval such as terminal bench.
- 383toast 7mo agoyeah 4tok/s is kinda unusable though
- deleted 7mo ago[deleted]
- Aurornis 7mo agoReading the details, he is using 2-bit quantization and reduced the number of experts per token from 10 down to 4 to get 5 tokens/sec. Cool proof of concept but it’s far from the quality and performance of the 397B model as normally used. Dropping the number of experts is particularly misleading. This is some interesting work, but applying such extreme measures to LLMs to get them to run severely degrades quality. I know he claims negligible quality loss, but in my experience 2-bit quantizations are completely useless for real work. You can get them to respond to prompts, but they lose their intelligence and will go around in circles. He also shows 5-6 tokens per second. Again that’s impressive for a large model on limited hardware but it’s very slow. Between the severely degraded model abilities and the extremely slow output the 397B result should be considered an attempt at proving something can technically run, not evidence that it can run well and produce output you’d expect from a 397B model. He even mentions the obvious problems with his changes: > *2-bit quantization produces \name\ instead of "name" in JSON output, making tool calling unreliable. So right out of the gate this isn’t useful if you want to do anything with it. He could have tried smaller models or less quantizations to get actual useful output from the model, but it wouldn’t look as impressive. It’s honestly getting kind of exhausting to read all of these AI-coded (admitted in the link) and AI-written papers made more for resume building. It would have been interesting to see this work applied to running a useful model that hadn’t been lobotomized instead of applying tricks to get an impressive headline but useless output.
- kageroumado 7mo ago[dead]
- 190n 7mo ago> *2-bit quantization produces \name\ instead of "name" in JSON output, making tool calling unreliable. I was wondering about that statement. Shouldn't it restrict sampling to only tokens that produce valid JSON matching the schema during a tool call? On the other hand, I have heard a lot about how even production LLM providers don't always call tools accurately, so I suppose either it's hard to implement what I described or there's something I haven't thought of that makes it impossible.
- bearjaws 7mo ago
- leontloveless 7mo ago[dead]
- maxloh 7mo agoCan you add a license to the repo? Legally we couldn't run any code without a license attached to it.
- Wowfunhappy 7mo ago...you can't redistribute code without a license, but surely you can legally run it, can't you? Like, if I write a blog post and put it on my blog, you're allowed to read it, right? Heck, if my blog contains some Javascript code I wrote, I would imagine your web browser is allowed to run that code without opening you up to copyright infringement, even if I didn't provide an explicit license.
- freehorse 7mo agoThe author states that the code was written by Opus, and afaik AI-written code is not considered copyrightable. Without copyright on the code, there should not be something prohibiting you from running it or even redistributing it. Of course this may come down to the extent of human contribution.
- cml123 7mo agofwiw, the opposite view was recently taken for node js. GH hides most of the conversation by default due to size, but the gist is that a new VFS feature is being proposed for node that was largely written using an LLM. I think I've more often seen the view these days that if you used, steered, and likely modified code generated by an AI, you generated the code and hold the copyright. https://github.com/nodejs/node/pull/61478 https://github.com/nodejs/node/pull/61478 https://blog.platformatic.dev/why-nodejs-needs-a-virtual-file-system https://blog.platformatic.dev/why-nodejs-needs-a-virtual-fil...
- m-hodges 7mo agoAs frontier models get closer and closer to consumer hardware, what’s the most for the API-driven $trillion labs?
- stri8ted 7mo ago48 GB is not consumer hardware. But fundamentally, there are economies of scale due to batching, power distribution, better utilization etc.., that means data center tokens will be cheaper. Also, as the cost of training (frontier) models increases, it's not clear the Chinese companies will continue open sourcing them. Notice for example, that Qwen-Max is not open source.
- zozbot234 7mo agoNothing obviously prevents using this approach, e.g. for 3B-active or 10B-active models, which do run on consumer hardware. I'd love to see how the 3B performs with this on the MacBook Neo, for example. More relevantly, data-center scale tokens are only cheaper for the specific type of tokens data centers sell. If you're willing to wait long enough for your inferences (and your overall volume is low enough that you can afford this) you can use approaches like OP's (offloading read-only data to storage) to handle inference on low-performing, slow "edge" devices.
- m-hodges 7mo ago> 48 GB is not consumer hardware. It’s a MacBook.
- kelnos 7mo agoTechnically that's correct (which as we all know is the best kind of correct), but really, how many consumers are buying a high-end MacBook Pro with 48GB or more of RAM? That's a very small percentage of the population. In these kinds of discussions, "consumer" is being used as a proxy for "something your average home laptop buyer might have". And a 48GB MBP is not that. I know it's annoying, because a 48GB MBP is indeed technically "consumer hardware", but please understand the context and don't be pedantic. You know what the GP meant. (And if not, that's... kinda on you.)
- diablevv 7mo ago[flagged]
- justacatbot 7mo ago[flagged]
- deleted 7mo ago[deleted]
- haomingkoo 7mo agoReally interesting approach. Curious how the 2-bit quantization affects the model's reasoning ability on longer chains of thought vs shorter prompts. The benchmarkslook solid but real-world usage seems like a different story based on the comments here.
- jee599 7mo ago[dead]
- genie3io 7mo ago[dead]
- mkw 7mo agoTLDR I took a stab at leveraging Dan's work and making it more practical: https://github.com/matt-k-wong/mlx-flash https://github.com/matt-k-wong/mlx-flash 2 bit quantization lobotomizes the model but is impressive nonetheless! Maybe one day we'll be able to have intelligent 2 bit quants... I wonder. my version supports - 4bit quantization, hybrid streaming (Disk + ram), arbitrary model compatibility, tested on Mamba2, and lets up the framework for LM Studio integration I leveraged this work (Credit to Danveloper) and am in the middle of making this work on more practical models and quants. It still uses flash streaming, but done so with a control knob so you can choose how much ram and how little ram to use. In the craziest case, it uses as little ram as possible but is very slow, however, in the balanced case you use some ram and it's much faster. I designed it around the intelligence dense Nemotron 3 Nano 30B and Nemotron Cascade 2 30B models (which are smaller, more intelligence density) and can run on low end 16GB machines, though you can run arbitrarily large models on larger machines (designed for very low end, but capable of high end).
- robutsume 7mo ago[dead]
- leontloveless 7mo ago[dead]
- mannyv 7mo agoEveryone is focused on the bad 2 bit result but who cares? He says don’t use it because it’s bad.
- Aurornis 7mo agoIf you don’t care about the output, why not reduce to 1-bit and only 1 active expert? It will be completely useless but it will be faster!
- qiine 7mo agoIt seem strange to me that the only way to use an llm is to fit it entirely in volatile memory from the get go. To render movies we happily wait for the computer to calculate how lights bounce around, for hours even days. So why not do the same with AIs? Ask big question to big models and get the answer to the universe tomorrow?
- andoando 7mo agoThere's definitely use cases for this for long running tasks, like doing research, but for typical use cases they require way too much constant supervision and interaction
- Aurornis 7mo agoIf you don’t care about turnaround time you can do that. Most LLM use cases are about accelerating workflows. If you have to wait all night for a response and then possibly discover that it took the wrong direction, misunderstood your intent, or your prompt was missing some key information then you have to start over. I don’t let LLMs write my code but I do a lot of codebase exploration, review, and throwaway prototyping. I have hundreds to maybe thousands of turns in the LLM conservation each day. If I had to wait 10X or 100X as long then it wouldn’t be useful. I’d be more productive ignoring a slow LLM and doing it all myself.
- qiine 7mo ago"If you have to wait all night for a response and then possibly discover that it took the wrong direction, misunderstood your intent, or your prompt was missing some key information then you have to start over." This exact problem exist for rendering, when you realize that after a long render an object was missing in the background and the costly frame is now useless. To counter that you make multiple "draft" renders first to make sure everything is in the frame and your parameters are properly tuned.
- zozbot234 7mo ago> If you have to wait all night for a response and then possibly discover that it took the wrong direction, misunderstood your intent, or your prompt was missing some key information then you have to start over. If you have to wait overnight because the model is offloading to disk, that's a model you wouldn't have been able to run otherwise without very expensive hardware. You haven't really lost anything. If anything, it's even easier to check on what a model is doing during a partial inference or agentic workload if the inference process is slower.
- andai 7mo ago> Metal Compute Shaders — Hand-written Metal kernels Hand written... by GPT? ;)
- aplomb1026 7mo ago[dead]
- feshbach 7mo ago[dead]
- matchbox 7mo agothis is awesome Dan!
- Yanko_11 7mo ago[dead]
- leontloveless 7mo ago[dead]
- jllyhill 7mo agoTo be honest, I'm getting tired of a "laptop" in every one of these clickbait titles turning out to be $3000 Macbook. Sure, it's impressive to achieve this degree of the LLM compression, but I really don't like that the title implies local LLM becomes a viable for an average person with the actual hardware being out of reach for 99%.
- Computer0 7mo agoYeah I understand the sentiment, I think it should’ve been “,on a laptop!” instead of “on a laptop”
- throw284959 7mo agoI ran full version of this model without any swapping on cluster of 2x $3000 laptops (strix halo zbook 128GB) at about 20 tokens per second. I would say it is in reach for normal person. If anything buying it was great investment, it is work tool, I will probably sell it for more than what I bought it for :)
- prmoustache 7mo ago> I would say it is in reach for normal person. That is a very wealthy country centric thing to say.
- mycall 7mo ago..and to think $2=3k is basically a minimum to really use local LLMs effectively, with prices definitely rising globally. Most people will just stick with online services for LLMs.
- kroaton 7mo agoIt could just as easily be a $3000-4000 Strix Halo laptop.
- freehorse 7mo agoYou can probably go lower than $3000, I expect, ime, an M1 max with 64GB ram to have similar performance, and you can find such one used with less than $2000 or so probably. In any case, I do not think that the range of people that have an M1/2/3/4 max macbook is that narrow, eg people who may do video editing or who benefit from having one of the fastest multicore laptops. It is handy to be able to do work with a machine you already may own for separate reasons, though it is definitely more to the side of a "pro device" than "basic consumer device".
- qcautomation 7mo ago[dead]
- fluxist 7mo ago[dead]
- breakingcups 7mo ago> No Python. No frameworks. Just C, Objective-C, and hand-tuned Metal shaders. Welp, I know where those tokens came from.
- leontloveless 7mo ago[dead]
- gregfrank 7mo ago[dead]
- aplomb1026 7mo ago[dead]
- thestack_ai 7mo ago[dead]
- openclaw01 7mo ago[dead]
- 999900000999 7mo agoIf I have a dedicated GPU with 12GB of VRAM and 32 GB of system ram, can I combine the two for LLMs. So far ollama will use the 12GB and then give up
- ranger_danger 7mo agollama.cpp can do exactly that
- arikrahman 7mo ago[dead]
- RandyOrion 7mo agoThis project shows an interesting automated search for engineering problems that I like to see more. The experience of utilizing tiered storage (gpu vram, ram, and ssd) is generally poor for a lot of LLM inference engines out there, e.g., llama.cpp, sglang, vllm, etc.. My own experience shows that both weight and KV cache offload to ram on sglang and vllm is unavailable or unusable. Copying extra parameters from documents and adding them to already working commands results in errors. Llama.cpp does support weight offload, but the experience is not pleasant, low pcie (gpu <-> ram) utilization, low gpu utilization, and really low tokens per second.
- jee599 7mo ago[dead]
- laalshaitaan 7mo ago4.4 tok/s with reliable structured output is a solid local benchmark altho the question is whether SSD streaming introduces per-token latency variance that messes up tool call parsing downstream. The gap between 400 GB/s unified memory bandwidth and 17.5 GB/s SSD reads means you're in the hot path pretty much every time an expert isn't cached.
- NamlchakKhandro 7mo agolmao 4.4 tokens per second is hilariously and utterly bad. anyone suggesting that it's a reasonable speed should find another career
- zozbot234 7mo agoThis reads like an unfairly shallow dismissal. 4.4 tok/s is more than par for the course (even acknowledging that the model was heavily quantized and stripped down by limiting active experts) for something that's streaming weights from storage in order to run on vastly more limited hardware than the model was designed for. It's even more impressive since the OP writeup mentions Apple Silicon hardware cannot overlap GPU compute with SSD access.
- ActorNightly 7mo agoThe problem is that the hardware is still like $3000. Making anything run on Macs is an exercise in futility. And its a shame that people get duped into buying Macs for LLM inference.
- zozbot234 7mo ago$3000 for running a 397B total parameters model is quite a bargain. The Mac is being used for its access to fast internal storage here since that's the key bottleneck, you could probably achieve similar outcomes with conventional (even fairly low-end) iGPU/APU hardware plus a fast PCIe x4 5.0 SSD (which would also allow you to overlap SSD transfers with iGPU/APU compute), but the cost would also be in a similar range. (Unless you carefully chose low-end e.g. Intel hardware with proper PCIe x4 5.0 NVMe support - which is still quite uncommon, especially for laptops.)
- ActorNightly 7mo agoIf you want to flex on being able to run 397b parameter models at unusably slow tokens/second sure. You can buy a 3090 for $2k, and run QWEN3.5 at 50+ token a second, and it will do everything you need, especially if you give it enough context.
- jee599 7mo ago[dead]
- techpulse_x 7mo ago[flagged]
- Yanko_11 7mo ago[dead]
- claud_ia 7mo ago[dead]
- utopiah 7mo agoI honestly don't get "why" despite having done similar things myself, e.g. run on a model on a VR headset itself. I mean I've done it because I could, so I imagine others are doing that too. But then... once it's done I don't actually use it. I ticked that box but eventually when STOA aren't that useful I have a hard time imagining actual positive use cases (... not like offline spam or naughty chat in the woods) that would benefit from such technically impressive demos.
- QubridAI 7mo ago[dead]
- maxothex 7mo ago[dead]
- dmonterocrespo 7mo ago[dead]
- druide67 7mo agoThe finding about removing the 9.8 GB Metal LRU cache for a 38% speedup is the most interesting part. Same lesson as PostgreSQL's advice against application-level buffer pools that compete with the OS page cache : the hardware memory compressor doing 130K decompressions/sec was pure overhead. Curious about the remaining gap: 5.7 tok/s vs 18.6 theoretical (from SSD bandwidth). Is the ~70% overhead mostly GPU compute on non-expert layers (attention, norm), or is there I/O scheduling room left?