Love this, even if can't use it atm (not got the h/w - only 96gb on M2 Max). I get it the general comp/public will find it unusable or worse. Reminds me of how home computers were - mere toys - before they became personal computers (PC). On my h/w the only passable combo for me atm is pi agent + llama.cpp + nemotron cascade-2 model: to 1M context, hybrid arch doesn't crash & burn 1/N^2 with context depths of 10K-50K-100K used by code agents. Was on a plane without Internet the other day. Brought a smile to my face that I could run pi agent (with llama.cpp serving), and it was just about usable at 40-30 tok/s. Afaik the usual API speeds are double that, 60-80 tok/s. Sensors showing using 60W when running inference. So battery probably would not last more than >3h. Model only 30B in size leaves plenty of space for KV-caches, and other programs - even at generous 8-bit quant. Only 3B active params at one time (with MoE A3B) is about the most that ageing M2 Max can carry it seems.
> even if can't use it atm (not got the h/w - only 96gb on M2 Max).
Not sure if it works different on macOS, but with CUDA + DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix.gguf I can fit it within 96GB of VRAM, together with context, so theoretically I feel like you should too, unless macOS uses GB of RAM/VRAM for the OS/display by default.
On 96gb I can give up to about 88GB to the GPU with sysctl iogpu.wired_limit_mb=88000, without suffering any ill-effects. When pushed higher I tend to notice e.g. graphic driver errors, youtube web page not working, other semi-random glitches. So the ~80 GB of DS4-flash quants I could just about fit. Leaving some extra for the KV caches. Will try, I'm curious how's the DS4 degradation with context depth growth, how fast does tok/s drop. E.g. 2-bit lowest quant MiniMax-M2.6 runs, but starts low tok/s and degrades fast with context depth.
The biggest models I can comfortably run are about 1/2 the DS4F size - like gpt-oss-120b. Lately was toying with Ling-2.6-flash. Got the agents to adapt existing metal kernels in llama.cpp, and it did run (model https://huggingface.co/ljupco/Ling-2.6-flash-GGUF, branch https://github.com/ljubomirj/llama.cpp/tree/LJ-Ling-2.6-flas...). It's 104B-A7B4, and for the M2 Max 7.4B active is about the most it can take while still producing 40 tok/s. And the hybrid arch allows for graceful degradation, still close to 30 tok/s at 64K context depth.
Too bad L2.6F while the best have, is not that much better in agentic benchmarks compared to my current incumbent local llm (nemotron-cascade-2). Got inspired by DS4 to start a l26f branch (WIP https://github.com/ljubomirj/l26f). :-) Try squeeze the most from L2.6F. There should be low hanging fruit in good integration of the agent and the inferencing engine. On input - considering the huge difference cached v.s. non-cached tokens. On output - considering that the NN gives us the complete logits set for all 200K+ tokens vocabulary.
Thanks for the DS4, will give it a try. Was hoping maybe I can re-quantise shave few GB... MiniMax-M2.7 Unsloth's UD-IQ2_XXS is down to 65GB - it run albeit too slow to be usable to an agent at context depth. I'm curious DS4F with it being economical with the KV caches - if that translates into keeping up with context. Was hoping 80GB 2-bit quants maybe come down to 70GB... that would be more comfortable to run.
