Yes, just — 1.0 GB spare

gpt-oss 120B at MXFP4 fits your Instinct MI210 entirely on the GPU at 8K context, at an estimated 144 tokens per second. Past 36K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

Fully on GPU 8K context MXFP4 · 60.5 GB Apache 2.0 Released Aug 2025 Native MXFP4

Designed to land on one 80 GB card. Only ~5B parameters are active per token.

What hardware do I need for gpt-oss 120B? →

The VRAM budget

weights 60.5 GB

Weights 60.5 GB KV cache @ 8K 0.29 GB Runtime overhead 0.6 GB Free 1.0 GB of 62.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
MXFP4	60.5 GB	61.4 GB	36K	144	Reference	Recommended

Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. This model interleaves sliding-window layers (128 tokens, 1 global in 2), which is why its cache barely grows with context.

How to run it

Ollama llama.cpp LM Studio vLLM

terminal

$ llama-server \
    -hf openai/gpt-oss-120b:MXFP4 \
    -c 8192 -ngl 99

The engine underneath most of the others. Every knob is exposed. More on llama.cpp.

01Download is 60.5 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03Only 1.0 GB is spare, so a long system prompt can still push it over. Its real ceiling here is 36K context.

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