3 ms·
Shapelearn Qwen 3.8 27B (13.1 GB VRAM)
- sheo 8d agoAged like milk https://news.ycombinator.com/item?id=49746618 https://news.ycombinator.com/item?id=49746618
- Mashimo 8d agoIn the comments it reads like bonsei falls apart on longer running tasks.
- txrx0000 8d agoNot really. The largest IQ4_XS quant here is still worth it because Bonsai doesn't offer larger quants. They could beat it if they made a quaternary variant though, I don't know why they're stopping at ternary.
- rguiscard 8d agoI wonder the same thing for Bonsai 2. ByteShape offers 5 models from IQ2_XXS-2.56bpw (8.8GB), IQ3_XXS-2.88bpw (9.9GB), IQ3_XS-3.01bpw (10.4GB), IQ3_S-3.23bpw (11.0GB) to IQ4_XS-3.84bpw (13.1GB). Their benchmarks show gradual improvement with size and users can pick one to fit theirs need. Bonsai-2-27B now is about 8.6GB. It might be good to have a quaternary version around 10-11GB to fit a computer with 16-24GB RAM.
- deleted 8d ago[deleted]
- dingdingdang 8d agoAgree here, as it is ByteShape wins practicality wise if the goal is doing actual work with these quants!
- electroglyph 8d agoprismml's title is very misleading. in their own paper the model is at 75% of coding scores.
- _ache_ 8d agoFrom my own test. It's not faster than the unsloth model. Disclarer: I'm unsing Vulkan on an AMD GC.
- DiabloD3 8d agoSurprised its not meaningfully slower. Vulkan and ROCm paths are missing a few optimized versions of the quants they're using.
- GrayShade 8d agoI see around 1 tk/s after a while, so perhaps it is.
- Systemerror7A69 8d agoAMD 7900 XTX with Vulkan here as well, wasn't faster on my test either. Might be much different on Nvidia though. I assume the limit for me is memory bandwith, as the 7900 XTX has the same bandwith as the 3090 from what I can gather and I already reached ~60 t/s with Unsloth. Those would fit with the numbers Byteshape has for their cards. 4090 and 5090 have much higher bandwith apparently, so on those cards you can probably get much more out of the kinds of performance improvements they are doing.
- noir_lord 8d ago4090 isn't that much higher than the XTX (I also have the XTX), it's 1008GB/s (4090) vs 960GB/s for the XTX's. The 5090 destroys both at 1792GB/s. It's not really one thing with the nvidia cards best I can tell it's that they compounded incremental gains from software drivers, card kernels and optimization from been the primary choice (plus first mover advantage). I didn't buy the XTX for AI purely gaming but it's a capable enough local card for running Qwen et al.
- Figs 8d ago4090 vs 5090 performance difference is largely GDDR6 vs GDDR7, I think
- Schlagbohrer 8d agoAbsolute treasure of a website with these graphs, thank you for sharing this. Huge help for me to find a faster model (smaller quantization) for my VRAM.
- kristianp 8d agoWhat's GPU-5?
- LtdJorge 8d agoThe fattest quantization. They show all of them here: https://huggingface.co/byteshape/Qwen3.8-27B-GGUF https://huggingface.co/byteshape/Qwen3.8-27B-GGUF Since they’re not using a stable number of bits per token, they use their own naming convention.
- noir_lord 8d ago> they use their own naming convention. Seems like a lot of them do, I only compare them within the same repo because there doesn't seem to be a very standard way of saying all the possible combinations/rearrangements.
- iker00 8d agobpw is the way to compare. huggingface has standard tags that must be used so it forces anyone releasing models to choose a tag that doesn't necessarily equal the actual bpw.
- tancop 8d agoIt's a bad way to compare. Average bpw ignores the fact that some layers can tolerate more aggressive quant than others.
- syntaxing 8d agoI’m on a strix halo @ GPU-5 with MTP and I get 600 prefill and 30 TG which pushes it into a very usable range. The odd thing is that Dflash2 is really slow for me, like sub 10 TG.
- naasking 8d agoI've found the opposite on my R9700 (n-max=7, no other speculative decoding like ngram-mod, which I found slows it down). I think it depends whether your workload and system are bandwidth limited or compute limited. I see draft acceptance around 0.55, so 0.55 * 7 = 3.8 tokens per pass, which on my bandwidth-limited card takes me from 30tps to a peak of 80tps on llama.cpp (MTP peaked at ~65tps). I'm also running a Qwen fine tune whose speculative execution is better than the base model. Strix Halo has lower compute than the R9700 but the RAM is also slower, so not sure what would be the ultimate limiting factor.
- syntaxing 8d agoCan you point me towards the model you use, both the main model and the flash model? Curious if I can get ~30 with a higher quant.
- naasking 8d agoModel, Q4_K_M: https://huggingface.co/agentionai/Signal-3.8-27B-GGUF https://huggingface.co/agentionai/Signal-3.8-27B-GGUF DFlash2, Q8_0, --spec-draft-n-max=7: https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2-GGUF https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2-GGUF I run llama.cpp with -ctv=8, -ctk=q4. Vulkan has better throughput if you're doing single-stream decode, but ROCm has better throughput if you have "--parallel 2" or higher. If supporting parallelism, unified kv cache should be off, especially with Vulkan. Of course, some of these may be specific to my card so try variations for your hardware. Hermes can concoct a test suite and run some tests for different llama.cpp parameter permutations to find something optimal.
- syntaxing 8d ago
- npodbielski 8d agoWell I tested it on 7900XTX with the same prompts and their draft model gave me about 30t/s. Their own snippet of code with regular MTP model gave me 60t/s. Also model with their draft answered incorrectly. With MTP it answered correctly. Question was: "Does MikroTik CRS312-4C+8XG-RM have combo ports?". The answer is Yes.
- nvme0n1p1 8d agoThat's not how you're supposed to use LLMs. You shouldn't expect a tiny little local model to know random facts about every obscure consumer product on earth. That's the job of tool calling. At best a model of this size is just giving you a random guess. You're basically saying "I tried rolling these dice one time, the green dice rolled a 6 and the blue dice rolled a 1, so green dice are better"
- serf 8d agoagreed. a niche knowledge callout is about the worst benchmark one can give a smaller model. smaller models are attempting to distill the useful methodologies, not the license plate number of an obscure extras car on Magnum PI. that said I wonder if there is a small 'trivia' model out there. Seems like the kinda thing Google would tackle.
- npodbielski 8d agoWhich was not he point because I was testing their solution for MPT and it was just funny addition. But of course in internet you always will find some 'well akchually' person straight from the meme.
- npodbielski 8d agoWhen I changed the number of draft tokens to 3 in both, it helped and they Draft is actually performing a bit better: - draft: 67.17 - MTP: 64.18 Why they used those examples? Seems strange.