Two Qwen3 models on one DGX Spark: the residency math(devashish.me) |
Two Qwen3 models on one DGX Spark: the residency math(devashish.me) |
Now at 40-50tok/s generation and ~2000 tok/s prefill with a model that I've seen reason through race conditions and be able to trivially pull off any straight-forward coding task, and remain coherent at 500k context. With a preview checkpoint of the weights!
I'm excited for the future of local LLMs. There is some buy-in but apparently not an extreme amount to get access to models that can stand in the for the giants on all but the most challenging and/or hands-off coding tasks.
Not clear how you went from ~11-14 to ~40-50 tok/s. Is it by running the quant native model and adding a second spark?
Cheers
Assuming that’s not true based on your phrasing, you’d be shooting yourself in the foot. Start using online models with the same quant at least benchmark as what you could run at home. Prepare for the at home model to be slower.
You don't even need to go that far. For example, with Exoscale Dedicated Inference[1] you just point it at the Hugging Face for the model and quantisation you want to test and it automagically spits out an OpenAI-compatible API endpoint.
[1] https://www.exoscale.com/ai-cloud-infrastructure/dedicated-i...
(I have no relationship with Exoscale, this particular product just crossed my radar recently)
Edit: 3.6 not 3.7!
While experimenting with quantization I found that there is a non-trivial tradeoff between quality and memory footprint. Overall my experience follows the reported pattern of "2-bit is mwah, 4-bit half decent and 6-bit required for programming. Still, although MiniMax-m2.7 is useable with the 6-bit quantizations that unsloth provides, it felt like such a breath of fresh air when I used the reference full-size model.
I find it difficult to say why. I had mostly the same setup as before (parsing had to be slightly adjusted in Zed). Aside from not experiencing the thinking loops (where minimax would get stuck generating the same sentences over and over) there is little evidence of any real improvement (although the average thinking time felt shorter).
I would recommend against very low quantizations of GLM 5.0/5.1/5.2 or Kimi 2.5/2.6. Smaller models were more reliable, and therefore more useful.
I've run 2 qwen/gemma @8bit with full context window side-by-side. Right now I have 4 models on my spark (qwen36moe, embedding, reranker, qwen3-1.7B) to support my markdown kb tool.
The setup is not as capable, but still good and gets better with models/algos. To me, it's more about the freedom to tinker, freedom from token bill anxiety, and potential right to compute should the government/oligarchy decides it gets to decide who can access which models.
The tool_choice="auto" failure on Qwen3-Next isn't a parser issue — the model reasons inside <think>, decides, and never emits the tool call. No error, just empty tool_calls. The fix was swapping the backbone from Thinking to Instruct, not tuning any parser flag.
The "load the bigger model first, size the smaller against actual residency" playbook generalizes to anything with shared CUDA framework overhead. The ~5 GiB framework floor shows up even at small gpu_memory_utilization values — plan against actuals, not targets.