Can the DGX Spark (GB10) run Qwen3.6 35B A3B?

Yes. Qwen3.6 35B A3B fits the DGX Spark (GB10)'s 128 GB at Q4_K_M (about 22.8 GB needed), running at an estimated 60 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
128 GB
Needs at Q4
22.8 GB
Headroom
105.2 GB
Speed (Q4_K_M)
~60 tok/s

Every quantization

Qwen3.6 35B A3B (36B, 3B active) on 128 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M21 GB22.8 GBFits easily~60 tok/s
Q5_K_M26 GB27.8 GBFits easily~48 tok/s
Q8_039 GB40.8 GBFits easily~32 tok/s
FP1672 GB73.8 GBFits easily~17 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the DGX Spark (GB10)'s 273.2 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf unsloth/Qwen3.6-35B-A3B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3.6 35B A3B details →

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Same model, other hardware - and other models on this card

Qwen3.6 35B A3B on other hardware

Also runs on the DGX Spark (GB10)

Frequently asked

Quick answers

Can the NVIDIA DGX Spark (GB10) run Qwen3.6 35B A3B?

Yes. Qwen3.6 35B A3B fits the DGX Spark (GB10)'s 128 GB at Q4_K_M (about 22.8 GB needed), running at an estimated 60 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Qwen3.6 35B A3B need?

About 21 GB of weights at Q4_K_M, 39 GB at Q8_0 and 72 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen3.6 35B A3B on the DGX Spark (GB10)?

Roughly 60 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the DGX Spark (GB10)'s 273.2 GB/s memory bandwidth; as a mixture-of-experts model only about 3B parameters are read per token, which is why it is faster than its size suggests.