ToolSite gpt-oss-120b VRAM requirements
gpt-oss-120b has 116.8B parameters, of which about 5.1B are used per token; all 128 experts still have to be in memory. With an 8K-token context and one request it needs about 73.2 GB of GPU memory at Q4_K_M, 121 GB at FP8 and 240 GB at FP16/BF16. The published weights take 60.8 GB (MXFP4). It does not fit on a 24 GB card at Q4_K_M; the smallest single GPU that holds it with an 8K context is the 80 GB A100 / H100 80GB.
Open gpt-oss-120b in the calculator
VRAM by quantization
Weights plus the KV cache for 8,192 tokens in FP16 and the runtime overhead (0.5 GB plus 10%). Each row opens the calculator with that setting. What the GGUF names mean.
| Precision | Weights | Total | Smallest setup |
|---|---|---|---|
| As published (MXFP4) | 60.8 GB | 67.7 GB | A100 / H100 80GB |
| FP16 / BF16 | 218 GB | 240 GB | 2 × H200 |
| FP8 / INT8 | 109 GB | 121 GB | H200 |
| INT4 (AWQ / GPTQ) | 57.8 GB | 64.4 GB | A100 / H100 80GB |
| GGUF Q8_0 | 116 GB | 128 GB | H200 |
| GGUF Q6_K | 89.2 GB | 99.0 GB | H200 |
| GGUF Q5_K_M | 77.1 GB | 85.6 GB | H200 |
| GGUF Q4_K_M | 65.8 GB | 73.2 GB | A100 / H100 80GB |
| GGUF Q3_K_M | 53.2 GB | 59.3 GB | A100 / H100 80GB |
| GGUF Q2_K | 45.6 GB | 50.9 GB | A100 / H100 80GB |
KV cache at long context
18 of its 36 layers use full attention and 18 keep a sliding window of 128 tokens. Each extra token of context adds 36 KB of FP16 cache per request once the sliding windows are full. How the KV cache works.
| Context | KV cache, FP16 | KV cache, FP8 | Total at Q4_K_M |
|---|---|---|---|
| 4K tokens | 149 MB | 74 MB | 73.1 GB |
| 32K tokens | 1.13 GB | 578 MB | 74.2 GB |
| 128K tokens | 4.50 GB | 2.25 GB | 77.9 GB |
Which GPUs can run gpt-oss-120b
With 8,192 tokens of context. Several GPUs means one tensor-parallel group of 2, 4 or 8 cards.
| GPU | Memory | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 | 12 GB | Needs 8 | Needs more than 8 |
| RTX 4060 Ti 16GB | 16 GB | Needs 8 | Needs 8 |
| RTX 3090 / 4090 | 24 GB | Needs 4 | Needs 8 |
| RTX 5090 | 32 GB | Needs 4 | Needs 4 |
| A100 40GB | 40 GB | Needs 2 | Needs 4 |
| Mac, 64 GB unified memory about 75% of it is usable by the GPU by default | 48 GB | Needs 2 | Needs 4 |
| L40S / RTX 6000 Ada | 48 GB | Needs 2 | Needs 4 |
| A100 / H100 80GB | 80 GB | Fits on one | Needs 2 |
| Mac, 128 GB unified memory about 75% of it is usable by the GPU by default | 96 GB | Fits on one | Needs 2 |
| H200 | 141 GB | Fits on one | Fits on one |
| B200 | 180 GB | Fits on one | Fits on one |
How fast gpt-oss-120b writes
Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth and the parameters read per token. A dash means it does not fit on one card. Try other settings in the speed calculator.
| Hardware | Bandwidth | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 12GB | 360 GB/s | — | — |
| RTX 4090 | 1,008 GB/s | — | — |
| RTX 5090 | 1,792 GB/s | — | — |
| M4 Max Mac (128 GB) | 546 GB/s | 45–77 | — |
| M3 Ultra Mac Studio (512 GB) | 819 GB/s | 65–114 | 43–73 |
| H100 SXM | 3,350 GB/s | 205–396 | — |
| H200 | 4,800 GB/s | 259–523 | 190–363 |
Longest context on one GPU
How many tokens of context fit on a single card with one request and an FP16 KV cache. "Full" means the model's whole context window fits.
| GPU | Memory | Q4_K_M | Q8_0 | FP8 |
|---|---|---|---|---|
| RTX 3060 | 12 GB | No | No | No |
| RTX 4060 Ti 16GB | 16 GB | No | No | No |
| RTX 3090 / 4090 | 24 GB | No | No | No |
| RTX 5090 | 32 GB | No | No | No |
| A100 40GB | 40 GB | No | No | No |
| Mac, 64 GB unified memory | 48 GB | No | No | No |
| L40S / RTX 6000 Ada | 48 GB | No | No | No |
| A100 / H100 80GB | 80 GB | 128K (full) | No | No |
| Mac, 128 GB unified memory | 96 GB | 128K (full) | No | No |
| H200 | 141 GB | 128K (full) | 128K (full) | 128K (full) |
| B200 | 180 GB | 128K (full) | 128K (full) | 128K (full) |
Model details
- Parameters
- 116.8B (116,829,156,672)
- Experts
- 128 routed experts, all loaded
- Active per token
- 5.1B
- Layers
- 18 of its 36 layers use full attention and 18 keep a sliding window of 128 tokens
- Attention cache
- 8 KV heads × 64
- Context length
- 131,072 tokens
- Published weights
- 60.8 GB (MXFP4)
- On Hugging Face
- openai/gpt-oss-120b
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Numbers read from the model files on Hugging Face on .