5 ms·

Two Qwen3 models on one DGX Spark: the residency math

95 points by devashish86 4mo ago

devashish86 4mo ago
Author here. Quick context the post doesn't quite spell out: 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.
- barrkel 3mo ago
  Can you try and tune your Claude or whatever LLM you're using for your text to phrase things in plain English. Way less use of antithesis, at least. You can probably find a skill for it, if not get an LLM to write your own.
  - reasonableklout 3mo ago
    Yes, there are lots of obvious LLM tells that don't add value, like "the math has to be empirical, not aspirational", use of colorful technical language like "knobs" and "wiring", etc. It distracts from the content.
    - devashish86 3mo ago
      fair points. will take this into account in the next post.
- edg5000 3mo ago
  From the Codex system prompt (verbatim): ``` (...) - Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like \"I will do <this good thing> rather than <this obviously bad thing>\", \"I will do <X>, not <Y>\". - Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query. (...) ``` It seems the OpenAI people added that first bullet to specifically address the tendency the model has, as seen in the parent comment. The goblin stuff coincidentally appears right after in the system prompt, so in included it as a bonus.
  - dofm 3mo ago
    FWIW the mere fact that the goblin stuff is necessary and is in the system prompt suggests to me that OpenAI's approach of training ridiculously large models that can do everything for everyone is hopelessly cooked. Though I concede it is not that much different than straightening the tie of your most valuable employee before you unwisely put them in front of a client and saying "please don't tell them about the regressions they didn't notice and remember, they don't want things explained in allegories drawn from the Silmarillion".
    - edg5000 3mo ago
      Indeed. Maybe in the future labs will be more distinct in what they care about most (and what their model is best at), rather than trying to max out all benchmarks. This may happen once we see finetuned GLM/Kimi/DeepSeek companies enter the market. I think it's not happening yet because of the hardware supply chain issues.
shireboy 3mo ago
I’ve been considering a move to local llm setup, having been underwhelmed coat vs value of various online offerings. But at the same time worried anything I get will be obsolete in a couple months. And I don’t want to have to babysit it. I really want some agents managing and creating side hustles for me and have some other things. I’m technical-have written my own harness and use gh copilot and grok daily and have a hosted openwebui+openrouter thing. I’m also torn between a 128g MacBook Pro or a framework, or spark or similar and lightweight laptop to access. Would love advice anyone has for (or against) going local. I have asked ai but have analysis paralysis as 5k would be a big investment for me so I want to make right choices
- peddling-brink 3mo ago
  Well, if you are making side-hustle money now using online models that, critically, you could also run at home, then it sounds like it’s just a matter of numbers. Oh and, unless you spend a lot more than 5k, your local model will still be slower than the online model. What’s your estimated ROI? 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.
  - dominotw 3mo ago
    no one is making money side-hustling ai models. This is like reddit wet dream. get real, dont get scammed by ppl selling you these dreams.
  - shireboy 3mo ago
    My thought process is that I don’t mind a slower model if it can work in background for me 24/7 fleshing out side gig ideas I have floating around but no time to focus on myself. I take your point though, and it’s why I haven’t bit the bullet yet. I could buy a lot of tokens for 5k. If I could make that effective then the roi of offline should be something I can calculate fairly easily.
- ericd 3mo ago
  You probably want to try renting some time on a dedicated box with roughly the specs you’re considering and running the open models for a bit to see if you would actually use them before dropping a lot on local hardware. A 128 gig MacBook Pro isn’t going to get you an amazing model, and certainly not amazing speed. GLM 5.2 wants something like 350+ gigs at fp4 iirc.
dzink 3mo ago
Have you tried llama.cpp with unsloth and models suited to it? GLM flash? It seemed to allow more models to be tried soon after they are released. Haven’t tried for long term deployment though, that’s the next step.
- pet_the_bird 3mo ago
  Highy anecdotal: I have tried various self-hosted models using both vllm and llama.cpp. I am in a situation where I have access to large amount of memory (~320 GB). 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.
  - embedding-shape 3mo ago
    I only have access to 96GB VRAM locally, but I'd agree with the general approach of avoiding lower quantizations, often anything below Q8 seems to suffer greatly on quality and seemingly never worth going below it, better to go for smaller model in that case. With the exception of DwarfStar + DS4-Flash with IQ2_XXS quantization, which somehow seems to not suffer as much as I'd thought. I'd still opt for a smaller model + at least Q8.
- verdverm 3mo ago
  I have tried llama-cpp, vllm is nicer (ray, handles queueing, doesn't have the cache invalidation bug for qwen/gemma models) and unsloth has toxic employees in their discord. 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.
roger_ 3mo ago
How about Qwen3.6? What sort of prefill/decode rates? Edit: 3.6 not 3.7!
- simonw 3mo ago
  So far there aren't any open weight model releases for the Qwen 3.7 family.
  - syhol 3mo ago
    > So far Someone's optimistic
    - simonw 3mo ago
      I'm hoping the decision makers at Qwen notice how influential the 3.6 series is while the 3.7 series has had very little attention at all. (Of course for all I know the 3.7 series is doing incredibly well in China, but I've seen almost no buzz around it from the circles that I inhabit.)
      - CamperBob2 3mo ago
        My impression is that with the latest round of high-profile releases, the open-weight "market" is coalescing around two players, DS4 Flash for speed and GLM 5.2 for smarts. Qwen is being left behind to pick up the scraps for the terminally GPU-poor. We know they have what it takes to fight back, and they know it... so I agree, there's no reason not be optimistic about future Qwen releases. But then I've never really understood what motivates these releases in the first place.
        simonw 3mo ago
        Qwen still have the best models that actually run on a laptop - Gemma 4 is their best competition there.
        3mo ago
wolttam 3mo ago
I started with antirez' DwarfStar[1] on one spark and that (~11-14tok/s generation, ~300-400 tok/s prompt processing) was enough of a taste for me to jump into 2 sparks, running the native quant of DSv4 Flash. 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. [1]: https://github.com/antirez/ds4 https://github.com/antirez/ds4
- binyu 3mo ago
  > Now at 40-50tok/s generation and ~2000 tok/s 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
  - wolttam 3mo ago
    I suspect DwarfStar could probably squeeze more performance out of the single spark, maybe up closer to 20tok/s. Moving to 2 sparks meant switching to vLLM with 2-way tensor parallelism and working multi-token prediction. The parallelism and MTP on top of better tuned kernels[1] gave an extremely nice boost! I was quite pleased. I've seen bursts up to 60tok/s at ~150k context - sometimes the MTP seems to really kick in (i.e. high acceptance rate on its tokens) Currently running a custom vLLM build put together by some folks on the Nvidia forums[2], which speaks to how early support for the model is. [1]: https://github.com/lukealonso/b12x https://github.com/lukealonso/b12x [2]: https://forums.developer.nvidia.com/t/372268 https://forums.developer.nvidia.com/t/372268
    - binyu 3mo ago
      Personally, I've tried to squeeze more tok/s for a single DGX Spark deployment and DeepSeek V4 Flash but only got marginal improvements. There's work to do on fusing kernels and other optimizations that are already on antirez's roadmap so it is not worth duplicating efforts. I've had positive experiences running GLM 4.7 via vLLM, tool calling works well and the inference is fast. Do you run DeepSeek V4 Flash on vLLM?
softwareseko 3mo ago
[flagged]
cws_ai_buddy 3mo ago
[flagged]
treis 3mo ago
2 LLMs at the same time? I've always wanted to do that
deleted 3mo ago
[deleted]
jimmypk 3mo ago
[flagged]
Myrmornis 3mo ago
The article was clearly written by an LLM. Please say so at the top.
- devashish86 3mo ago
  fair point, i'll publish the methodology in the beginning next time onward. fwiw, this is my high level process. 1. i keep comprehensive notes _while_ i'm experimenting(like checkpoints). this is a mix of commits and a append-only changelog file 2. if some part of what i've done seems like i can share with public, i create an outline of it. small paragraph with a few points 3. then i ask the model to merge the two to generate a post. i have my own style guide but ofcourse, the model idiosyncrasies will always creep in.
codelong888 3mo ago
[flagged]
m_sharma 3mo ago
I plan to buy a DGX Spark, so this was a very useful read. Interesting to learn that a 128GB-class device doesn't really give you the full memory budget for models once you account for CUDA overhead.

95 points by devashish86 4mo ago

Author here. Quick context the post doesn't quite spell out: 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.

barrkel 3mo ago
Can you try and tune your Claude or whatever LLM you're using for your text to phrase things in plain English. Way less use of antithesis, at least. You can probably find a skill for it, if not get an LLM to write your own.
- reasonableklout 3mo ago
  Yes, there are lots of obvious LLM tells that don't add value, like "the math has to be empirical, not aspirational", use of colorful technical language like "knobs" and "wiring", etc. It distracts from the content.
  - devashish86 3mo ago
    fair points. will take this into account in the next post.
edg5000 3mo ago
From the Codex system prompt (verbatim): ``` (...) - Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like \"I will do <this good thing> rather than <this obviously bad thing>\", \"I will do <X>, not <Y>\". - Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query. (...) ``` It seems the OpenAI people added that first bullet to specifically address the tendency the model has, as seen in the parent comment. The goblin stuff coincidentally appears right after in the system prompt, so in included it as a bonus.
- dofm 3mo ago
  FWIW the mere fact that the goblin stuff is necessary and is in the system prompt suggests to me that OpenAI's approach of training ridiculously large models that can do everything for everyone is hopelessly cooked. Though I concede it is not that much different than straightening the tie of your most valuable employee before you unwisely put them in front of a client and saying "please don't tell them about the regressions they didn't notice and remember, they don't want things explained in allegories drawn from the Silmarillion".
  - edg5000 3mo ago
    Indeed. Maybe in the future labs will be more distinct in what they care about most (and what their model is best at), rather than trying to max out all benchmarks. This may happen once we see finetuned GLM/Kimi/DeepSeek companies enter the market. I think it's not happening yet because of the hardware supply chain issues.

shireboy 3mo ago

I’ve been considering a move to local llm setup, having been underwhelmed coat vs value of various online offerings. But at the same time worried anything I get will be obsolete in a couple months. And I don’t want to have to babysit it. I really want some agents managing and creating side hustles for me and have some other things. I’m technical-have written my own harness and use gh copilot and grok daily and have a hosted openwebui+openrouter thing. I’m also torn between a 128g MacBook Pro or a framework, or spark or similar and lightweight laptop to access. Would love advice anyone has for (or against) going local. I have asked ai but have analysis paralysis as 5k would be a big investment for me so I want to make right choices

peddling-brink 3mo ago
Well, if you are making side-hustle money now using online models that, critically, you could also run at home, then it sounds like it’s just a matter of numbers. Oh and, unless you spend a lot more than 5k, your local model will still be slower than the online model. What’s your estimated ROI? 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.
- dominotw 3mo ago
  no one is making money side-hustling ai models. This is like reddit wet dream. get real, dont get scammed by ppl selling you these dreams.
- shireboy 3mo ago
  My thought process is that I don’t mind a slower model if it can work in background for me 24/7 fleshing out side gig ideas I have floating around but no time to focus on myself. I take your point though, and it’s why I haven’t bit the bullet yet. I could buy a lot of tokens for 5k. If I could make that effective then the roi of offline should be something I can calculate fairly easily.
ericd 3mo ago
You probably want to try renting some time on a dedicated box with roughly the specs you’re considering and running the open models for a bit to see if you would actually use them before dropping a lot on local hardware. A 128 gig MacBook Pro isn’t going to get you an amazing model, and certainly not amazing speed. GLM 5.2 wants something like 350+ gigs at fp4 iirc.

dzink 3mo ago

Have you tried llama.cpp with unsloth and models suited to it? GLM flash? It seemed to allow more models to be tried soon after they are released. Haven’t tried for long term deployment though, that’s the next step.

pet_the_bird 3mo ago
Highy anecdotal: I have tried various self-hosted models using both vllm and llama.cpp. I am in a situation where I have access to large amount of memory (~320 GB). 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.
- embedding-shape 3mo ago
  I only have access to 96GB VRAM locally, but I'd agree with the general approach of avoiding lower quantizations, often anything below Q8 seems to suffer greatly on quality and seemingly never worth going below it, better to go for smaller model in that case. With the exception of DwarfStar + DS4-Flash with IQ2_XXS quantization, which somehow seems to not suffer as much as I'd thought. I'd still opt for a smaller model + at least Q8.
verdverm 3mo ago
I have tried llama-cpp, vllm is nicer (ray, handles queueing, doesn't have the cache invalidation bug for qwen/gemma models) and unsloth has toxic employees in their discord. 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.

roger_ 3mo ago

How about Qwen3.6? What sort of prefill/decode rates? Edit: 3.6 not 3.7!

simonw 3mo ago
So far there aren't any open weight model releases for the Qwen 3.7 family.
- syhol 3mo ago
  > So far Someone's optimistic
  - simonw 3mo ago
    I'm hoping the decision makers at Qwen notice how influential the 3.6 series is while the 3.7 series has had very little attention at all. (Of course for all I know the 3.7 series is doing incredibly well in China, but I've seen almost no buzz around it from the circles that I inhabit.)
    - CamperBob2 3mo ago
      My impression is that with the latest round of high-profile releases, the open-weight "market" is coalescing around two players, DS4 Flash for speed and GLM 5.2 for smarts. Qwen is being left behind to pick up the scraps for the terminally GPU-poor. We know they have what it takes to fight back, and they know it... so I agree, there's no reason not be optimistic about future Qwen releases. But then I've never really understood what motivates these releases in the first place.
      - simonw 3mo ago
        Qwen still have the best models that actually run on a laptop - Gemma 4 is their best competition there.
        3mo ago

wolttam 3mo ago

I started with antirez' DwarfStar[1] on one spark and that (~11-14tok/s generation, ~300-400 tok/s prompt processing) was enough of a taste for me to jump into 2 sparks, running the native quant of DSv4 Flash. 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. [1]: https://github.com/antirez/ds4 https://github.com/antirez/ds4

binyu 3mo ago
> Now at 40-50tok/s generation and ~2000 tok/s 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
- wolttam 3mo ago
  I suspect DwarfStar could probably squeeze more performance out of the single spark, maybe up closer to 20tok/s. Moving to 2 sparks meant switching to vLLM with 2-way tensor parallelism and working multi-token prediction. The parallelism and MTP on top of better tuned kernels[1] gave an extremely nice boost! I was quite pleased. I've seen bursts up to 60tok/s at ~150k context - sometimes the MTP seems to really kick in (i.e. high acceptance rate on its tokens) Currently running a custom vLLM build put together by some folks on the Nvidia forums[2], which speaks to how early support for the model is. [1]: https://github.com/lukealonso/b12x https://github.com/lukealonso/b12x [2]: https://forums.developer.nvidia.com/t/372268 https://forums.developer.nvidia.com/t/372268
  - binyu 3mo ago
    Personally, I've tried to squeeze more tok/s for a single DGX Spark deployment and DeepSeek V4 Flash but only got marginal improvements. There's work to do on fusing kernels and other optimizations that are already on antirez's roadmap so it is not worth duplicating efforts. I've had positive experiences running GLM 4.7 via vLLM, tool calling works well and the inference is fast. Do you run DeepSeek V4 Flash on vLLM?

softwareseko 3mo ago

[flagged]

cws_ai_buddy 3mo ago

[flagged]

treis 3mo ago

2 LLMs at the same time? I've always wanted to do that

deleted 3mo ago

[deleted]

jimmypk 3mo ago

[flagged]

Myrmornis 3mo ago

The article was clearly written by an LLM. Please say so at the top.

devashish86 3mo ago
fair point, i'll publish the methodology in the beginning next time onward. fwiw, this is my high level process. 1. i keep comprehensive notes _while_ i'm experimenting(like checkpoints). this is a mix of commits and a append-only changelog file 2. if some part of what i've done seems like i can share with public, i create an outline of it. small paragraph with a few points 3. then i ask the model to merge the two to generate a post. i have my own style guide but ofcourse, the model idiosyncrasies will always creep in.

codelong888 3mo ago

[flagged]

m_sharma 3mo ago

I plan to buy a DGX Spark, so this was a very useful read. Interesting to learn that a 128GB-class device doesn't really give you the full memory budget for models once you account for CUDA overhead.