4 ms·

I'm currently fiddling with a DGX Spark and Qwen3.6-35B-A3B (specifically Qwen3.6-35B-A3B-NVFP4 under vLLM, with EAGLE3 speculative decoding via eagle3-dogacel-

by lee_ars 3mo ago

I'm currently fiddling with a DGX Spark and Qwen3.6-35B-A3B (specifically Qwen3.6-35B-A3B-NVFP4 under vLLM, with EAGLE3 speculative decoding via eagle3-dogacel-vllm), and it's pretty okay in terms of smarts. The speed is relatively usable at about 50 tok/sec with a 256k context window, and it's definitely smart enough to one-shot some basic coding tasks. I had it doing reverse engineering/disassembly of some ancient MS-DOS assembly language games from the 80s and it handled the task well and produced good outputs. But it's also really easy to trip up. I fed it some of my Ars pieces and asked it to analyze themes and composition, and it got into a looping argument with me over how it was unable to analyze "my" writing because "the user cannot be the article author, the user is the user, the user did not write the article, the article author wrote the article." I was utterly unable to convince it that I was in fact me. Qwen3.6-35B-A3B hums along at about 50GB of RAM used with --gpu-memory-utilization=0.42. I haven't tried Qwen3.6-27B (I'd likely grab Qwen3.6-27B-FP8, I think), but I'm curious to see if it makes much of a difference.

rnxrx 3mo ago
There are also nvfp4 quants of Qwen 3.6 27/35 floating around. I've done benchmarks of both and the quality difference vs fp8/bf16 was barely notable. Honestly the nvfp4 capability is the most interesting feature of the Spark (at least for me).
anon373839 3mo ago
I use Qwen 3.6 35B-A3B constantly, but I don’t see the type of behavior you mentioned. I’m using Unsloth’s Q8_K_XL quant.
cpburns2009 3mo ago
Looping is a common problem with the Qwen models. I've had good luck using --repeat-penalty=1.1 with llama.cpp and 27B. vLLM should have a similar option.
- etdznots 3mo ago
  This is the default value!
  - cpburns2009 3mo ago
    Llama.cpp defaults to 1.0 (disabled) and so does vLLM. It looks like only ollama defaults to 1.1.
- Der_Einzige 3mo ago
  Please switch to using the far superior reptation penalty, DRY. It's built into llamacpp.
  - cpburns2009 3mo ago
    What are good DRY setting
coder543 3mo ago
Compared to a dynamic quant like Unsloth's UD-Q4_K_XL, which keeps some important parameters in higher precision, a basic NVFP4 quant seems to do a lot more damage to the model unless it is carefully calibrated. I would recommend using llama-server if you're just on a single Spark. You get access to dynamic quants like that more easily, the performance is not that different from vLLM most of the time these days, and it is much faster and easier to switch between models. As far as intelligence goes, Qwen3.6-27B is much smarter than the 35B-A3B model, but that's also not the sort of thing to argue with an AI model about in the first place. Just open a new chat and try again. Gemma-4-31B is not as good at agentic use cases as Qwen3.6-27B, but it is a fairly balanced model overall, and worth trying out too. Its MTP can nearly triple the performance of the model, where the benefits of MTP or Eagle seem more limited for Qwen3.6-27B in my testing, maybe doubling the speed.
- Der_Einzige 3mo ago
  [flagged]
  - coder543 3mo ago
    It’s not FUD. It is my actual, lived experience. FUD is false, which this is not. I use both vLLM and llama-server. vLLM is very painful, even with the Spark community docker image. It is slow to start, it does not support 3-bit dynamic quants well, and it takes a lot of tweaking to get it to run well for each model I want to try out, which is made worse by the slow starts. I’m glad you’ve had a better experience? I can only speak to the experiences that I have had repeatedly. For at least a month, people on the official Spark forum were claiming you just couldn’t run MiMo-V2.5 on a single Spark, because they refused to use anything other than vLLM, while I was doing it just fine on llama-server with 200k+ of context. And llama-server is “worse” in what specific ways? I was specific with my comment. The usual complaint was the lack of MTP/Eagle3 support in llama-server, but that is solved now. Now the main difference is a minor hit to prompt processing speed, at most, if you’re using a single Spark. Too many people on the Spark forum are closed minded to the idea that vLLM is not the solution to every problem. llama-server also comes with a truly excellent built-in web chat interface these days, which includes the ability to connect to MCPs so the models can be used agentically through a conversational interface even from my phone. What does vLLM offer? Yeah… nothing. And options like Open WebUI seem really bloated. For a cluster of multiple Sparks, the pain of vLLM is still worthwhile, as I already said before. Or if you’re running some kind of major production workload, I guess? Instead of a single user, few agent setup like most people.
gnerd00 3mo ago
`llama-server` looping mitigations --repeat-penalty something greater than 1.0, set reasoning/thinking OFF explicitly, prefer a gguf with more than 4bit quant
deleted 3mo ago
[deleted]

by lee_ars 3mo ago

rnxrx 3mo ago

There are also nvfp4 quants of Qwen 3.6 27/35 floating around. I've done benchmarks of both and the quality difference vs fp8/bf16 was barely notable. Honestly the nvfp4 capability is the most interesting feature of the Spark (at least for me).

anon373839 3mo ago

I use Qwen 3.6 35B-A3B constantly, but I don’t see the type of behavior you mentioned. I’m using Unsloth’s Q8_K_XL quant.

cpburns2009 3mo ago

Looping is a common problem with the Qwen models. I've had good luck using --repeat-penalty=1.1 with llama.cpp and 27B. vLLM should have a similar option.

etdznots 3mo ago
This is the default value!
- cpburns2009 3mo ago
  Llama.cpp defaults to 1.0 (disabled) and so does vLLM. It looks like only ollama defaults to 1.1.
Der_Einzige 3mo ago
Please switch to using the far superior reptation penalty, DRY. It's built into llamacpp.
- cpburns2009 3mo ago
  What are good DRY setting

coder543 3mo ago

Compared to a dynamic quant like Unsloth's UD-Q4_K_XL, which keeps some important parameters in higher precision, a basic NVFP4 quant seems to do a lot more damage to the model unless it is carefully calibrated. I would recommend using llama-server if you're just on a single Spark. You get access to dynamic quants like that more easily, the performance is not that different from vLLM most of the time these days, and it is much faster and easier to switch between models. As far as intelligence goes, Qwen3.6-27B is much smarter than the 35B-A3B model, but that's also not the sort of thing to argue with an AI model about in the first place. Just open a new chat and try again. Gemma-4-31B is not as good at agentic use cases as Qwen3.6-27B, but it is a fairly balanced model overall, and worth trying out too. Its MTP can nearly triple the performance of the model, where the benefits of MTP or Eagle seem more limited for Qwen3.6-27B in my testing, maybe doubling the speed.

Der_Einzige 3mo ago
[flagged]
- coder543 3mo ago
  It’s not FUD. It is my actual, lived experience. FUD is false, which this is not. I use both vLLM and llama-server. vLLM is very painful, even with the Spark community docker image. It is slow to start, it does not support 3-bit dynamic quants well, and it takes a lot of tweaking to get it to run well for each model I want to try out, which is made worse by the slow starts. I’m glad you’ve had a better experience? I can only speak to the experiences that I have had repeatedly. For at least a month, people on the official Spark forum were claiming you just couldn’t run MiMo-V2.5 on a single Spark, because they refused to use anything other than vLLM, while I was doing it just fine on llama-server with 200k+ of context. And llama-server is “worse” in what specific ways? I was specific with my comment. The usual complaint was the lack of MTP/Eagle3 support in llama-server, but that is solved now. Now the main difference is a minor hit to prompt processing speed, at most, if you’re using a single Spark. Too many people on the Spark forum are closed minded to the idea that vLLM is not the solution to every problem. llama-server also comes with a truly excellent built-in web chat interface these days, which includes the ability to connect to MCPs so the models can be used agentically through a conversational interface even from my phone. What does vLLM offer? Yeah… nothing. And options like Open WebUI seem really bloated. For a cluster of multiple Sparks, the pain of vLLM is still worthwhile, as I already said before. Or if you’re running some kind of major production workload, I guess? Instead of a single user, few agent setup like most people.

gnerd00 3mo ago

`llama-server` looping mitigations --repeat-penalty something greater than 1.0, set reasoning/thinking OFF explicitly, prefer a gguf with more than 4bit quant

deleted 3mo ago

[deleted]