text model · Qwen3 · Linux
Can I run Qwen3 235B A22B on Nvidia DGX Spark (128GB)?
No. Qwen3 235B A22B needs ~136.9 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
Needs ~136.9 GB even at Q4_K_M, but only ~112 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 24.9 GB: Qwen3 235B A22B needs roughly 136.9 GB at Q4_K_M and Nvidia DGX Spark (128GB) leaves only about 112 GB usable for a model. The lightest tracked hardware that runs Qwen3 235B A22B is the Apple M3 Ultra (256GB) at 256 GB. See Qwen3 235B A22B on Apple M3 Ultra (256GB).
- Q4_K_M needed
- ~136.9 GB
- Usable on device
- ~112 GB
- Device memory
- 128 GB
Which quant fits
- Parameters
- 235B (MoE, 22B active)
- Q4_K_M size
- 132.39 GB
- Q8_0 size
- 232.77 GB
- Context
- 128k
- Ollama tag
- qwen3:235b
- Memory
- 128 GB unified
- Usable for weights
- ~112 GB
- Power draw
- ~140 W
- Best runtime
- llama.cpp (CUDA) / Ollama (CUDA)
What you can run instead
Run Qwen3 235B A22B on other hardware
FAQ
Can Nvidia DGX Spark (128GB) run Qwen3 235B A22B?
No. Qwen3 235B A22B needs ~136.9 GB even at Q4_K_M, but Nvidia DGX Spark (128GB) only has ~112 GB usable.
How much memory does Qwen3 235B A22B need?
Nvidia DGX Spark (128GB) does not have enough memory. At Q4_K_M the weights are ~132.39 GB; with KV cache and runtime overhead, budget ~136.9 GB at a 4k context. It is a Mixture-of-Experts model (235B total / 22B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3 235B A22B 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/qwen3-235b-a22b/nvidia-dgx-spark-128gb) Sources
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aider.chat · 1 source
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docs.nvidia.com · 2 sources
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github.com · 1 source
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huggingface.co · 2 sources
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nvidia.com · 1 source
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nvidianews.nvidia.com · 1 source
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ollama.com · 3 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.