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
Get Started
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_K_M | 4.80 | 72.7 GB | 79.3 GB | 72.25 GB | 4-bit medium quantization — most popular sweet spot |
| 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.
Related Models
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 GBQ4_060.6 GBQ4_K_S68.1 GBQ4_K_M ★72.7 GBQ5_K_M86.2 GBQ8_0120.8 GB★ 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)
~40 tok/s~30 tok/s~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.
- Which GPUs can run GPT OSS 120B?
No single consumer GPU has enough VRAM to run GPT OSS 120B at Q4_K_M (72.7 GB). Multi-GPU or professional hardware is required.
- Which devices can run GPT OSS 120B?
5 devices with unified memory can run GPT OSS 120B at Q4_K_M (72.7 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.