gpt-oss-20b
gpt-oss-20b by OpenAI (21B parameters, MoE with 3.6B active per token). At Q4_K_M with an 8K context it needs about 13.1 GiB of memory. OpenAI's open-weight 21B MoE with 3.6B active and adjustable reasoning effort (low, medium, high); it fits a 16 GB card.
Last updated: 2026-10-13
Model facts
Run it
ollama run gpt-oss:20b
Memory needed
Weights + KV cache + runtime overhead, in GiB. Lower quantization = less memory, slightly lower quality.
| Quantization | 4K context | 8K context | 32K context |
|---|---|---|---|
| Q4_K_M | 13.0 GiB | 13.1 GiB | 13.7 GiB |
| Q5_K_M | 15.2 GiB | 15.3 GiB | 15.9 GiB |
| Q6_K | 17.5 GiB | 17.6 GiB | 18.2 GiB |
| Q8_0 | 22.4 GiB | 22.5 GiB | 23.1 GiB |
| FP16 | 41.7 GiB | 41.8 GiB | 42.3 GiB |
Which hardware runs it
Best quantization that fits each device with a 8K context. Estimates, not benchmarks. See the methodology.
Frequently asked questions
How much VRAM does gpt-oss-20b need?
With an 8K context, about 13.1 GiB at Q4_K_M and 22.5 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can gpt-oss-20b run on an GeForce RTX 4090?
Yes. gpt-oss-20b fits on the GeForce RTX 4090 (24 GB) at Q8_0 with an 8K context, using about 22.5 GiB, at an estimated 184 tokens/s.
Can I run gpt-oss-20b on a Mac?
Yes. gpt-oss-20b fits on the M3 Ultra (96 GB) at FP16 with an 8K context, using about 41.8 GiB, at an estimated 74 tokens/s.
Can gpt-oss-20b be used commercially?
gpt-oss-20b is released under Apache-2.0. The licence allows commercial use (read its conditions).