HomeCan I run it?gpt-oss-20bgpt-oss-20b on an Arc B580
No. gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the Intel Arc B580 has 12.0 GB usable at a ~4k context.
Can I run gpt-oss-20b on an Arc B580?
gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the Intel Arc B580 has 12.0 GB usable. That figure is 12 GB of VRAM on the spec sheet, of which 12.0 GB is usable for weights and cache, at 456 GB/s memory bandwidth. OpenAI’s open 20B-class gpt-oss model. Only a few billion parameters are active per token, so decode can be quicker than a dense 20B. These are estimates from our formula, not measured benchmarks.
- Quant that fits
- None of Q4, Q8 or FP16 needs 15.6 GB
- Memory needed
- 15.6 GB at Q4_K_M, ~4k context
- Usable VRAM
- 12.0 GB of 12 GB VRAM
- Est. tokens/sec
- n/a does not fit
Memory at each quant
| Quant | Need vs 12.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 15.6 GB | Q4Won’t fit |
| Q8_0 | 26.6 GB | Q8Won’t fit |
| FP16 | 49.1 GB | FP16Won’t fit |
Estimated tokens per second
No speed estimate: gpt-oss-20b does not fit in the 12.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.
Context headroom
Even at a short ~4k context, the weights do not fit. Extending context would only increase the gap.
| Context | KV cache (FP16) | Total at Q4_K_M | Verdict |
|---|---|---|---|
| 4k | 0.1 GB | 15.6 GB | Won’t fit |
| 8k | 0.2 GB | 15.7 GB | Won’t fit |
| 16k | 0.4 GB | 15.9 GB | Won’t fit |
| 32k | 0.8 GB | 16.3 GB | Won’t fit |
| 64k | 1.6 GB | 17.1 GB | Won’t fit |
| 128k | 3.2 GB | 18.7 GB | Won’t fit |
What to do instead
- Run R1 Distill 14B instead (11.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
- To hold gpt-oss-20b at Q4, you need about 15.6 GB. The GeForce RTX 4060 Ti 16GB has 16.0 GB usable ($499 list price).
- Our estimate is only 3.6 GB over this machine’s usable memory, so a tighter quant or a shorter context may work in practice. We still mark it as a no under the published formula.
- CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
- See the best GPU for this model family and the gpt-oss-20b model page.
Other hardware for gpt-oss-20b
- gpt-oss-20b on an RTX 3060 12GBWon’t fit
- gpt-oss-20b on an RTX 3070 TiWon’t fit
- gpt-oss-20b on an RTX 3080Won’t fit
- gpt-oss-20b on an RTX 3090Fits
- gpt-oss-20b on an RTX 4060 Ti 16GBTight
- gpt-oss-20b on an RTX 4070 Ti SuperTight
Other models on an Arc B580
- Qwen3-32B on an Arc B580Won’t fit
- Qwen3-30B-A3B on an Arc B580Won’t fit
- Qwen3.8-27B on an Arc B580Won’t fit
- Gemma 3 27B on an Arc B580Won’t fit
- Gemma 4 31B on an Arc B580Won’t fit
- Mistral Small 24B on an Arc B580Won’t fit
Quick answers
- Can I run gpt-oss-20b on an Arc B580?
- No. gpt-oss-20b at Q4_K_M needs about 15.6 GB, and the Intel Arc B580 has 12.0 GB usable.
- How much VRAM does gpt-oss-20b need?
- About 15.6 GB at Q4_K_M, 26.6 GB at Q8_0 and 49.1 GB at FP16, estimated for a ~4k context.
- How fast is gpt-oss-20b on an Arc B580?
- We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.
- What context length can I use?
- The weights already do not fit, so longer context is not the limiting factor.