Can the DGX Spark (GB10) run gpt-oss-120b?

Yes. gpt-oss-120b fits the DGX Spark (GB10)'s 128 GB at Q4_K_M (about 71.8 GB needed), running at an estimated 35 tok/s. There's room for up to Q5_K_M if you want higher quality.

VRAM
128 GB
Needs at Q4
71.8 GB
Headroom
56.2 GB
Speed (Q4_K_M)
~35 tok/s

Every quantization

gpt-oss-120b (120.4B, 5.1B active) on 128 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M70 GB71.8 GBFits easily~35 tok/s
Q5_K_M85 GB86.8 GBFits~29 tok/s
Q8_0129 GB130.8 GBToo big -
FP16241 GB242.8 GBToo big -

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.

Compare

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

gpt-oss-120b on other hardware

Also runs on the DGX Spark (GB10)

Frequently asked

Quick answers

Can the NVIDIA DGX Spark (GB10) run gpt-oss-120b?

Yes. gpt-oss-120b fits the DGX Spark (GB10)'s 128 GB at Q4_K_M (about 71.8 GB needed), running at an estimated 35 tok/s. There's room for up to Q5_K_M if you want higher quality.

How much VRAM does gpt-oss-120b need?

About 70 GB of weights at Q4_K_M, 129 GB at Q8_0 and 241 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is gpt-oss-120b on the DGX Spark (GB10)?

Roughly 35 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 5.1B parameters are read per token, which is why it is faster than its size suggests.