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.
3.8M downloads
4.9K likesSpecifications
- Publisher
- OpenAI
- Family
- GPT-OSS
- Parameters
- 120.4B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-08-04
- 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 |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
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 GB18 devices with unified memory can run GPT OSS 120B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download GPT OSS 120B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →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 241.2 GB at BF16. 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_M ★72.7 GBQ5_K_S83.2 GBQ5_K_M86.2 GBBF16241.2 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 ~61 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 72.7 × 0.65 = ~72 tok/s
Estimated speed at Q4_K_M (72.7 GB)
~72 tok/s~72 tok/s~61 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 BF16 version is 240.82 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?
19 devices with unified memory can run GPT OSS 120B at Q4_K_M (72.7 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.