gpt-oss-120b
gpt-oss-120b by OpenAI (117B parameters, MoE with 5.1B active per token). At Q4_K_M with an 8K context it needs about 70.1 GiB of memory. OpenAI's 117B MoE with only 5.1B active, so it's quick for its size; it needs a 72 GB+ card or a 96–128 GB unified-memory machine.
Last updated: 2026-10-13
Model facts
| Developer | OpenAI |
|---|---|
| Parameters | 117B (5.1B active, MoE)[src] |
| Native context | 131,072 tokens[src] |
| Licence | Apache-2.0[src] |
| Commercial use | Yes |
| KV cache | 18 global + 18 sliding-window layers (128 tokens)[src] |
| Released | 2025-08 |
Good for: reasoning (adjustable effort) · tool use · coding · 80 GB GPU / 96–128 GB unified memory
Run it
ollama run gpt-oss:120b
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 | 70.0 GiB | 70.1 GiB | 71.0 GiB |
| Q5_K_M | 82.0 GiB | 82.2 GiB | 83.0 GiB |
| Q6_K | 94.9 GiB | 95.0 GiB | 95.9 GiB |
| Q8_0 | 122.2 GiB | 122.3 GiB | 123.2 GiB |
| FP16 | 229.5 GiB | 229.6 GiB | 230.5 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-120b need?
With an 8K context, about 70.1 GiB at Q4_K_M and 122.3 GiB at Q8_0 (weights + KV cache + runtime overhead). Longer contexts need more memory for the KV cache.
Can gpt-oss-120b run on an GeForce RTX 4090?
gpt-oss-120b does not fit on the GeForce RTX 4090 (24 GB), even at Q4_K_M with an 8K context.
Can I run gpt-oss-120b on a Mac?
Yes. gpt-oss-120b fits on the M3 Ultra (96 GB) at Q4_K_M with an 8K context, using about 70.1 GiB, at an estimated 172 tokens/s.
Can gpt-oss-120b be used commercially?
gpt-oss-120b is released under Apache-2.0. The licence allows commercial use (read its conditions).