GPT-oss 120B: VRAM Requirements & Local Setup Guide 2026
GPT-oss 120B is OpenAI's 117B-parameter open-weight model with a 131K tokens context window.
Quick Facts
- Provider
- OpenAI
- Architecture
- Dense Transformer
- Total Parameters
- 117B
- Active Params/Token
- Unknown
- Context Window
- 131K tokens
- License
- open
- Modalities
- text
- HuggingFace
- openai/gpt-oss-120b
VRAM Requirements by Quantization & Context Length
All values in gigabytes (GB). Calculated using the KV-cache formula with model-specific head fractions. “–” means the context length exceeds this model’s maximum context window. Lower quantization = less VRAM but slightly reduced output quality.
| Quantization | 4K ctx | 8K ctx | 32K ctx | 128K ctx |
|---|---|---|---|---|
| Q4_K_M | 68.1 GB | 68.3 GB | 69.1 GB | 72.5 GB |
| Q5_K_M | 79.3 GB | 79.4 GB | 80.3 GB | 83.6 GB |
| Q6_K | 91.1 GB | 91.3 GB | 92.1 GB | 95.5 GB |
| Q8_0 | 117.4 GB | 117.6 GB | 118.4 GB | 121.8 GB |
| FP16 | 219.6 GB | 219.7 GB | 220.6 GB | 223.9 GB |
Weights = 117B params × GGUF bits per weight (Q4_K_M ≈ 4.9); plus runtime overhead; plus fp16 KV cache from config.json: 8 KV heads × 64 dims (18 of 36 layers capped at a 128-token window) — ~72 KB per token at short context. Same engine as the Can I Run LLM calculator.
Best GPU for GPT-oss 120B by Budget
Recommendations assume Q4_K_M quantization at 4K context unless stated otherwise. Higher-end quantizations or longer context windows require more VRAM — consult the table above.
- Under $500 (Entry GPU) Not feasible at this budget
- $500–$1,000 (Mid-Range GPU) Not feasible at this budget
- $1,000–$3,000 (High-End GPU) Not feasible at this budget
- $3,000+ (Workstation / Cloud) A100 80GB (Q4_K_M)
Benchmark Scores
Scores reported by OpenAI or verified third-party evaluations. Higher is better for all benchmarks except where noted.
| Benchmark | Score |
|---|---|
| MMLU-Pro | 90 |
| GPQA Diamond | 80.9 |
| SWE-bench Verified | 62.4 |
| HumanEval | 88.3 |
| Arena Elo (Text) | 1366 |
| AIME 2024–25 (Epoch AI) | 88.9 |
| Aider Polyglot | 41.8 |
How to Run GPT-oss 120B Locally
Before downloading, verify your GPU has sufficient VRAM using the table above. Insufficient VRAM will cause the model to fall back to CPU offloading, which is significantly slower.
HuggingFace Hub
GPT-oss 120B is available on HuggingFace. Download with:
pip install huggingface_hub huggingface-cli download openai/gpt-oss-120b
Strengths and Use Cases
Strong at coding, math, reasoning, content writing. Runs locally with the right GPU — see the VRAM table above for exact requirements.
When choosing a local LLM, GPT-oss 120B is worth considering if your workload aligns with its design goals. As a Dense Transformer architecture from OpenAI, it offers a distinct trade-off between compute efficiency and capability. Its dense architecture provides consistent, predictable performance across a wide range of tasks. Whether you are building a local AI pipeline, experimenting with self-hosted chat, or running automated workflows, understanding this model's hardware envelope helps you plan infrastructure realistically.
For deployment, start with the Q4_K_M quantization unless you have headroom for higher precision. Q5_K_M and Q6_K offer improved output quality at the cost of additional VRAM. FP16 is generally only practical on high-VRAM workstation GPUs or cloud instances. Always verify context length requirements before selecting a quantization — longer context windows multiply KV-cache memory consumption significantly, as shown in the VRAM table above.
Ready to Check Your Hardware?
Use our interactive calculator to see exactly whether your GPU can run GPT-oss 120B — and at what quantization level.
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