GPT OSS 120B — Hardware Requirements & GPU Compatibility
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GPT-OSS 120B is the larger of OpenAI's open-source model releases, bringing 120.4 billion parameters of GPT-lineage capability to the open-weight ecosystem. It represents near-frontier performance across reasoning, knowledge, code generation, and conversational tasks, rivaling top proprietary offerings in many benchmarks. Running this model locally is a serious hardware commitment, typically requiring multiple high-VRAM GPUs or a professional-grade setup with 80+ GB of combined VRAM even at aggressive quantization levels. It is best suited for enthusiasts with multi-GPU rigs or workstation hardware who want the strongest possible local model from OpenAI's catalog.
4.7M downloads
4.6K likesSpecifications
- Publisher
- OpenAI
- Family
- GPT-OSS
- Parameters
- 120.4B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-08-26
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does GPT OSS 120B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 51.6 GB | 58.3 GB | 51.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 53.1 GB | 59.8 GB | 52.68 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 59.1 GB | 65.8 GB | 58.70 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 60.6 GB | 67.3 GB | 60.21 GB | 4-bit legacy quantization |
| Q4_1 | 4.50 | 68.1 GB | 74.8 GB | 67.73 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 68.1 GB | 74.8 GB | 67.73 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 72.7 GB | 79.3 GB | 72.25 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 83.2 GB | 89.9 GB | 82.78 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 86.2 GB | 92.9 GB | 85.79 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 99.8 GB | 106.4 GB | 99.34 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 120.8 GB | 127.5 GB | 120.41 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run GPT OSS 120B?
Q4_K_M · 72.7 GBGPT OSS 120B (Q4_K_M) requires 72.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 95+ GB is recommended. Using the full 131K context window can add up to 6.7 GB, bringing total usage to 79.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run GPT OSS 120B?
Q4_K_M · 72.7 GB5 devices with unified memory can run GPT OSS 120B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
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Frequently Asked Questions
- How much VRAM does GPT OSS 120B need?
GPT OSS 120B requires 72.7 GB of VRAM at Q4_K_M, or 120.8 GB at Q8_0. Full 131K context adds up to 6.7 GB (79.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 120.4B × 4.8 bits ÷ 8 = 72.2 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.1 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M72.7 GBQ4_K_M + full context79.3 GB- Can NVIDIA GeForce RTX 5090 run GPT OSS 120B?
No — GPT OSS 120B requires at least 51.6 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for GPT OSS 120B?
For GPT OSS 120B, Q4_K_M (72.7 GB) offers the best balance of quality and VRAM usage. Q5_K_S (83.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 51.6 GB.
VRAM requirement by quantization
Q2_K51.6 GB~75%Q4_060.6 GB~85%Q4_K_S68.1 GB~88%Q4_K_M ★72.7 GB~89%Q5_K_M86.2 GB~92%Q8_0120.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run GPT OSS 120B on a Mac?
GPT OSS 120B requires at least 51.6 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run GPT OSS 120B locally?
Yes — GPT OSS 120B can run locally on consumer hardware. At Q4_K_M quantization it needs 72.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT OSS 120B?
At Q4_K_M, GPT OSS 120B can reach ~40 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 72.7 × 0.55 = ~40 tok/s
Estimated speed at Q4_K_M (72.7 GB)
AMD Instinct MI300X~40 tok/sNVIDIA H100 SXM~30 tok/sAMD Instinct MI250X~25 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GPT OSS 120B?
At Q4_K_M, the download is about 72.25 GB. The full-precision Q8_0 version is 120.41 GB. The smallest option (Q2_K) is 51.18 GB.