Openai GPT OSS 20B GGUF — Hardware Requirements & GPU Compatibility
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9.7K downloads
33 likesSpecifications
- Publisher
- Bartowski
- Family
- GPT-OSS
- Parameters
- 20B
- Release Date
- 2025-08-11
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HuggingFace
How Much VRAM Does Openai GPT OSS 20B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 9.3 GB | — | 8.50 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 9.6 GB | — | 8.75 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 10.7 GB | — | 9.75 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 11 GB | — | 10.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 13.2 GB | — | 12.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 15.7 GB | — | 14.25 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 18.1 GB | — | 16.50 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 22 GB | — | 20.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Openai GPT OSS 20B GGUF?
Q4_K_M · 13.2 GBOpenai GPT OSS 20B GGUF (Q4_K_M) requires 13.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 18+ GB is recommended. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Openai GPT OSS 20B GGUF?
Q4_K_M · 13.2 GB27 devices with unified memory can run Openai GPT OSS 20B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Openai GPT OSS 20B GGUF need?
Openai GPT OSS 20B GGUF requires 13.2 GB of VRAM at Q4_K_M, or 22 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 20B × 4.8 bits ÷ 8 = 12 GB
KV Cache + Overhead ≈ 1.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M13.2 GB- What's the best quantization for Openai GPT OSS 20B GGUF?
For Openai GPT OSS 20B GGUF, Q4_K_M (13.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (13.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 6.0 GB.
VRAM requirement by quantization
IQ2_XXS6.0 GB~53%Q2_K9.3 GB~75%Q3_K_L11.3 GB~86%Q4_K_M ★13.2 GB~89%Q4_K_L13.5 GB~90%Q8_022.0 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Openai GPT OSS 20B GGUF on a Mac?
Openai GPT OSS 20B GGUF requires at least 6.0 GB at IQ2_XXS, 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 Openai GPT OSS 20B GGUF locally?
Yes — Openai GPT OSS 20B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 13.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Openai GPT OSS 20B GGUF?
At Q4_K_M, Openai GPT OSS 20B GGUF can reach ~221 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~50 tok/s. 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 ÷ 13.2 × 0.55 = ~221 tok/s
Estimated speed at Q4_K_M (13.2 GB)
AMD Instinct MI300X~221 tok/sNVIDIA GeForce RTX 4090~50 tok/sNVIDIA H100 SXM~165 tok/sAMD Instinct MI250X~137 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Openai GPT OSS 20B GGUF?
At Q4_K_M, the download is about 12.00 GB. The full-precision Q8_0 version is 20.00 GB. The smallest option (IQ2_XXS) is 5.50 GB.
- Which GPUs can run Openai GPT OSS 20B GGUF?
17 consumer GPUs can run Openai GPT OSS 20B GGUF at Q4_K_M (13.2 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 6 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Openai GPT OSS 20B GGUF?
27 devices with unified memory can run Openai GPT OSS 20B GGUF at Q4_K_M (13.2 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.