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Run LLM Locally

gpt-oss-120b on the A100 80GB

With 80 GB of VRAM, the A100 80GB runs gpt-oss-120b at MXFP4 entirely on the GPU with an 8K context (about 63.2 GB needed).

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gpt-oss-120b

MXFP4 · 8K context

Excellent

The model needs about 63.2 GB and fits in the 77.6 GB available, with 14.4 GB to spare.

Your hardware
A100 80GB · 64 GB RAM
Estimated memory
~63.2 GB estimate
VRAM (usable memory)
77.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
170–472 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: medium

Based on: same model · same quantization · other hardware, scaled by memory bandwidth · 21 benchmark(s)

Memory breakdown

~63.2 GB (61.6 GB–64.8 GB) needed · estimate

Fits entirely in VRAM · 14.4 GB left

  • Model weights59 GB
  • KV cache0.3 GB
  • Runtime overhead0.9 GB
  • Safety margin3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size · sliding-window layers keep 128 tokens

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • mixture-of-experts: speed depends on expert routing
  • scaled from other hardware

Other quantizations on the A100 80GB

gpt-oss-120b on A100 80GB + 64 GB RAM · 8K context
gpt-oss-120b MXFP48K~63.2 GB · 170–472 tok/s estimateExcellent

Alternatives

GPUs that run gpt-oss-120b MXFP4 entirely in VRAM

Other models that fit the A100 80GB

Hardware that runs gpt-oss-120b MXFP4 entirely in memory

Prices and links follow the market you choose.

Radeon PRO W7900

AMD

ExcellentRuns gpt-oss-120b MXFP4 entirely in memory

Price not available

Availability unknown

View on amazon.com

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NVIDIA DGX Spark (GB10)

Unified memory PC

ExcellentRuns gpt-oss-120b MXFP4 entirely in memory

Price not available

Availability unknown

View on amazon.com

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No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the A100 80GB run gpt-oss-120b?
The model needs about 63.2 GB and fits in the 77.6 GB available, with 14.4 GB to spare.
Which quantization should I use on the A100 80GB?
MXFP4 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~63.2 GB / 77.6 GB.
What is the largest context on the A100 80GB?
At MXFP4, about 128K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is gpt-oss-120b (A100 80GB)?
We estimate 170–472 tok/s (estimate, confidence medium). Based on: same model, same quantization, other hardware, scaled by memory bandwidth, 21 benchmark(s).