# gpt-oss-120b

> OnPrem World · https://onpremworld.com/en/models/gpt-oss-120b · Last updated: 2026-10-14 · CC BY 4.0

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.

|  |  |
|---|---|
| Developer | OpenAI |
| Parameters | 117B (5.1B active, MoE) |
| Native context | 131,072 tokens |
| Licence | Apache-2.0 |
| Commercial use | Yes |
| KV cache | 18 global + 18 sliding-window layers (128 tokens) |
| Released | 2025-08 |

```
ollama run gpt-oss:120b
```

## Memory needed

Weights + KV cache + runtime overhead, in GiB. Lower quantization = less memory, slightly lower quality.

| Quantization | 4K | 8K | 32K |
|---|---|---|---|
| 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 (8K)

| Hardware | Memory | Best quant | tok/s (est.) |
|---|---|---|---|
| RTX PRO 5000 72GB Blackwell | 72 GB | Q4_K_M | 304 |
| RTX PRO 6000 Blackwell Workstation Edition | 96 GB | Q6_K | 299 |
| M5 Max (18-core CPU / 40-core GPU) | 128 GB | Q6_K | 95 |
| M4 Max (16-core CPU / 40-core GPU) | 128 GB | Q6_K | 84 |
| M5 Ultra | 512 GB | FP16 | 76 |
| M3 Ultra | 512 GB | FP16 | 52 |
| RTX Spark (N1X) laptops & compact desktops | 128 GB | Q6_K | 43 |
| DGX Spark | 128 GB | Q6_K | 39 |
| Ryzen AI Max+ 395 (Strix Halo) systems | 128 GB | Q6_K | 37 |
| Ryzen AI Max+ PRO 495 (Gorgon Halo) systems | 192 GB | Q8_0 | 30 |
| GeForce RTX 3060 (12 GB) | 12 GB | Doesn't fit | – |
| GeForce RTX 3090 | 24 GB | Doesn't fit | – |
| GeForce RTX 4060 Ti 16GB | 16 GB | Doesn't fit | – |
| GeForce RTX 4080 SUPER | 16 GB | Doesn't fit | – |
| GeForce RTX 4090 | 24 GB | Doesn't fit | – |
| GeForce RTX 5060 Ti 16GB | 16 GB | Doesn't fit | – |
| GeForce RTX 5070 | 12 GB | Doesn't fit | – |
| GeForce RTX 5070 Ti | 16 GB | Doesn't fit | – |
| GeForce RTX 5080 | 16 GB | Doesn't fit | – |
| GeForce RTX 5090 | 32 GB | Doesn't fit | – |
| Radeon RX 7900 XT | 20 GB | Doesn't fit | – |
| Radeon RX 7900 XTX | 24 GB | Doesn't fit | – |
| Radeon RX 9060 XT 16GB | 16 GB | Doesn't fit | – |
| Radeon RX 9070 XT | 16 GB | Doesn't fit | – |
| Arc B580 | 12 GB | Doesn't fit | – |
| RTX A6000 | 48 GB | Doesn't fit | – |
| RTX 6000 Ada Generation | 48 GB | Doesn't fit | – |
| Radeon AI PRO R9700 | 32 GB | Doesn't fit | – |
| Arc Pro B60 | 24 GB | Doesn't fit | – |
| Arc Pro B70 | 32 GB | Doesn't fit | – |
| M4 Pro | 64 GB | Doesn't fit | – |
| M4 Max (14-core CPU / 32-core GPU) | 36 GB | Doesn't fit | – |
| M5 | 32 GB | Doesn't fit | – |
| M5 Pro | 64 GB | Doesn't fit | – |
| M5 Max (18-core CPU / 32-core GPU) | 36 GB | Doesn't fit | – |
| M6 (16GB configuration, 153 GB/s) | 16 GB | Doesn't fit | – |
| M6 (24GB/32GB configurations, 170 GB/s) | 32 GB | Doesn't fit | – |

## 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).

## Sources

- https://openai.com/index/introducing-gpt-oss/
- https://huggingface.co/openai/gpt-oss-120b
- https://huggingface.co/openai/gpt-oss-120b/resolve/main/config.json
- https://ollama.com/library/gpt-oss/tags