Skip to content
Run LLM Locally

gpt-oss-20b on the Arc A770 16 GB

With 16 GB of VRAM, the Arc A770 16 GB runs gpt-oss-20b at MXFP4 entirely on the GPU with an 8K context (about 13 GB needed).

ShareFacebookXWhatsAppLinkedInEmail

gpt-oss-20b

MXFP4 · 8K context

Excellent

The model needs about 13 GB and fits in the 15.5 GB available, with 2.5 GB to spare.

Your hardware
Arc A770 16 GB · 64 GB RAM
Estimated memory
~13 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
96–151 tok/s estimate
Largest context in fast memory
109K tokens
Confidence: low

Based on: memory bandwidth (560 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 2.5 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 (SYCL) · 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

  • no benchmark for this hardware and model yet
  • mixture-of-experts: speed depends on expert routing

Other quantizations on the Arc A770 16 GB

gpt-oss-20b on Arc A770 16 GB + 64 GB RAM · 8K context
gpt-oss-20b MXFP48K~13 GB · 96–151 tok/s estimateExcellent

Alternatives

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

Other models that fit the Arc A770 16 GB

Where to buy a Arc A770 16 GB

Prices and links follow the market you choose.

Intel Arc A770 16 GB

16 GB GDDR6 · 560 GB/s · 225 W

ExcellentRuns gpt-oss-20b MXFP4 entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

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

Frequently asked questions

Can the Arc A770 16 GB run gpt-oss-20b?
The model needs about 13 GB and fits in the 15.5 GB available, with 2.5 GB to spare.
Which quantization should I use on the Arc A770 16 GB?
MXFP4 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~13 GB / 15.5 GB.
What is the largest context on the Arc A770 16 GB?
At MXFP4, about 109K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is gpt-oss-20b (Arc A770 16 GB)?
We estimate 96–151 tok/s (estimate, confidence low). Based on: memory bandwidth (560 GB/s) and model size.