GPT OSS 120B GGUF — Hardware Requirements & GPU Compatibility
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21.7K downloads
19 likesSpecifications
- Publisher
- LM Studio Community
- Family
- GPT-OSS
- Parameters
- 120B
- 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 | 56.1 GB | — | 51.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 57.8 GB | — | 52.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 64.3 GB | — | 58.50 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 66 GB | — | 60.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 79.2 GB | — | 72.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 94.0 GB | — | 85.50 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 108.9 GB | — | 99.00 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 132 GB | — | 120.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run GPT OSS 120B GGUF?
Q4_K_M · 79.2 GBGPT OSS 120B GGUF (Q4_K_M) requires 79.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 103+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run GPT OSS 120B GGUF?
Q4_K_M · 79.2 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 79.2 GB of VRAM at Q4_K_M, or 132 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 120B × 4.8 bits ÷ 8 = 72 GB
KV Cache + Overhead ≈ 7.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M79.2 GB- Can NVIDIA GeForce RTX 5090 run GPT OSS 120B GGUF?
No — GPT OSS 120B GGUF requires at least 56.1 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 (79.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (90.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 56.1 GB.
VRAM requirement by quantization
Q2_K56.1 GB~75%Q4_066.0 GB~85%Q4_K_S74.3 GB~88%Q4_K_M ★79.2 GB~89%Q5_K_M94.0 GB~92%Q8_0132.0 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 56.1 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 79.2 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 ~37 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 ÷ 79.2 × 0.55 = ~37 tok/s
Estimated speed at Q4_K_M (79.2 GB)
AMD Instinct MI300X~37 tok/sNVIDIA H100 SXM~28 tok/sAMD Instinct MI250X~23 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.
- Which GPUs can run GPT OSS 120B GGUF?
No single consumer GPU has enough VRAM to run GPT OSS 120B GGUF at Q4_K_M (79.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run GPT OSS 120B GGUF?
5 devices with unified memory can run GPT OSS 120B GGUF at Q4_K_M (79.2 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.