text model · Qwen3.6 · Linux
Can I run Qwen3.6 35B-A3B on Nvidia DGX Spark (128GB)?
Yes. Qwen3.6 35B-A3B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~24.5 GB of ~112 GB usable).
Runs at Q4_K_M using ~24.5 GB of ~112 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia DGX Spark (128GB) leaves ~87.5 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~24.5 GB
- Usable on device
- ~112 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run qwen3.6:35b-a3b llama-cli -hf bartowski/Qwen_Qwen3.6-35B-A3B-GGUF:Q4_K_M lms get bartowski/Qwen_Qwen3.6-35B-A3B-GGUF - Parameters
- 36B (MoE, 3B active)
- Q4_K_M size
- 22.29 GB
- Q8_0 size
- 36.9 GB
- Context
- 256k
- Ollama tag
- qwen3.6:35b-a3b
- Memory
- 128 GB unified
- Usable for weights
- ~112 GB
- Power draw
- ~140 W
- Best runtime
- llama.cpp (CUDA) / Ollama (CUDA)
You could also run
Run Qwen3.6 35B-A3B on other hardware
FAQ
Can Nvidia DGX Spark (128GB) run Qwen3.6 35B-A3B?
Yes. Qwen3.6 35B-A3B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~24.5 GB of ~112 GB usable).
How much memory does Qwen3.6 35B-A3B need?
Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~22.29 GB; with KV cache and runtime overhead, budget ~24.5 GB at a 4k context. It is a Mixture-of-Experts model (36B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3.6 35B-A3B on Linux?
Ollama for a simple setup; vLLM for the most speed. vLLM shines for multi-user serving/throughput. For a single local chat, Ollama or llama.cpp is simpler and lighter.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/qwen3.6-35b-a3b/nvidia-dgx-spark-128gb) Sources
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docs.nvidia.com · 2 sources
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github.com · 1 source
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huggingface.co · 3 sources
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nvidia.com · 1 source
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nvidianews.nvidia.com · 1 source
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ollama.com · 2 sources
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tomshardware.com · 1 source
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.