GPT OSS 120B GGUF — Hardware Requirements & GPU Compatibility
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This is a GGUF-quantized version of OpenAI's GPT-OSS 120B, repackaged by Unsloth. GPT-OSS 120B is the larger variant of OpenAI's open-source model family, packing 120 billion parameters for significantly enhanced reasoning, knowledge, and generation capabilities compared to its smaller sibling. Unsloth's GGUF conversion enables this large model to run with llama.cpp and compatible tools. Even with aggressive quantization, a 120B-parameter model demands significant hardware, typically requiring multi-GPU setups or systems with very high VRAM capacity. For users with the hardware to support it, this model represents one of the most capable open-weight options available for local deployment.
83.7K downloads
229 likesSpecifications
- Publisher
- Unsloth
- Family
- GPT-OSS
- Parameters
- 120B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-08-25
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does GPT OSS 120B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 51.4 GB | 58.1 GB | 51.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 52.9 GB | 59.6 GB | 52.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 58.9 GB | 65.6 GB | 58.50 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 60.4 GB | 67.1 GB | 60.00 GB | 4-bit legacy quantization |
| Q4_1 | 4.50 | 67.9 GB | 74.6 GB | 67.50 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 67.9 GB | 74.6 GB | 67.50 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 72.4 GB | 79.1 GB | 72.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 82.9 GB | 89.6 GB | 82.50 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 85.9 GB | 92.6 GB | 85.50 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 99.4 GB | 106.1 GB | 99.00 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 120.4 GB | 127.1 GB | 120.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run GPT OSS 120B GGUF?
Q4_K_M · 72.4 GBGPT OSS 120B GGUF (Q4_K_M) requires 72.4 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 79.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run GPT OSS 120B GGUF?
Q4_K_M · 72.4 GB5 devices with unified memory can run GPT OSS 120B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Related Models
Frequently Asked Questions
- How much VRAM does GPT OSS 120B GGUF need?
GPT OSS 120B GGUF requires 72.4 GB of VRAM at Q4_K_M, or 120.4 GB at Q8_0. Full 131K context adds up to 6.7 GB (79.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 120B × 4.8 bits ÷ 8 = 72 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.1 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M72.4 GBQ4_K_M + full context79.1 GB- Can NVIDIA GeForce RTX 5090 run GPT OSS 120B GGUF?
No — GPT OSS 120B GGUF requires at least 51.4 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 GGUF?
For GPT OSS 120B GGUF, Q4_K_M (72.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (82.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 51.4 GB.
VRAM requirement by quantization
Q2_K51.4 GB~75%Q4_060.4 GB~85%Q4_K_S67.9 GB~88%Q4_K_M ★72.4 GB~89%Q5_K_M85.9 GB~92%Q8_0120.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run GPT OSS 120B GGUF on a Mac?
GPT OSS 120B GGUF requires at least 51.4 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 GGUF locally?
Yes — GPT OSS 120B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 72.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT OSS 120B GGUF?
At Q4_K_M, GPT OSS 120B GGUF 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 ÷ 72.4 × 0.55 = ~40 tok/s
Estimated speed at Q4_K_M (72.4 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 GGUF?
At Q4_K_M, the download is about 72.00 GB. The full-precision Q8_0 version is 120.00 GB. The smallest option (Q2_K) is 51.00 GB.