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Run LLM Locally

gpt-oss-20b on the RTX 6000 Ada

With 48 GB of VRAM, the RTX 6000 Ada runs gpt-oss-20b at MXFP4 entirely on the GPU with an 8K context (about 13 GB needed).

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gpt-oss-20b

MXFP4 · 8K context

Excellent

The model needs about 13 GB and fits in the 46.6 GB available, with 33.6 GB to spare.

Your hardware
RTX 6000 Ada 48 GB · 64 GB RAM
Estimated memory
~13 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
121–334 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

~13 GB (12.4 GB–13.6 GB) needed · estimate

Fits entirely in VRAM · 33.6 GB left

  • Model weights11.3 GB
  • KV cache0.2 GB
  • Runtime overhead0.9 GB
  • Safety margin0.6 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 RTX 6000 Ada

gpt-oss-20b on RTX 6000 Ada + 64 GB RAM · 8K context
gpt-oss-20b MXFP48K~13 GB · 121–334 tok/s estimateExcellent

Alternatives

GPUs that run gpt-oss-20b MXFP4 entirely in VRAM

Other models that fit the RTX 6000 Ada

Where to buy a RTX 6000 Ada

Prices and links follow the market you choose.

RTX 6000 Ada Generation

48 GB GDDR6 ECC · 960 GB/s · 300 W

ExcellentRuns gpt-oss-20b MXFP4 entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 6000 Ada run gpt-oss-20b?
The model needs about 13 GB and fits in the 46.6 GB available, with 33.6 GB to spare.
Which quantization should I use on the RTX 6000 Ada?
MXFP4 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~13 GB / 46.6 GB.
What is the largest context on the RTX 6000 Ada?
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-20b (RTX 6000 Ada)?
We estimate 121–334 tok/s (estimate, confidence medium). Based on: same model, same quantization, other hardware, scaled by memory bandwidth, 21 benchmark(s).