What's the best quantization for GPT OSS 20B GGUF?

For GPT OSS 20B GGUF, Q4_K_M (12.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (14.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 8.9 GB.

Can I run GPT OSS 20B GGUF on a Mac?

GPT OSS 20B GGUF requires at least 8.9 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 20B GGUF locally?

Yes — GPT OSS 20B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 12.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is GPT OSS 20B GGUF?

At Q4_K_M, GPT OSS 20B GGUF can reach ~236 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~53 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

What's the download size of 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 (Q2_K) is 8.50 GB.

Unsloth·GPT-OSS·GptOssForCausalLM

GPT OSS 20B GGUF — Hardware Requirements & GPU Compatibility

Chat

This is a GGUF-quantized version of OpenAI's GPT-OSS 20B, repackaged by Unsloth. GPT-OSS 20B is OpenAI's open-source model release, bringing the company's model-building expertise to the open-weight community with a 20-billion-parameter architecture. Unsloth's GGUF conversion makes this model compatible with llama.cpp and popular frontends like Ollama and LM Studio. At 20B parameters, it sits in a productive middle ground, large enough to deliver strong reasoning and generation quality while remaining runnable on consumer GPUs with 16GB or more of VRAM at appropriate quantization levels.

324.2K downloads 627 likesDec 2025131K context

Based on GPT OSS 20B

Specifications

Publisher: Unsloth
Family: GPT-OSS
Parameters: 20B
Architecture: GptOssForCausalLM
Context Length: 131,072 tokens
Vocabulary Size: 201,088
Release Date: 2025-12-19
License: Apache 2.0

Get Started

HuggingFace

unsloth/gpt-oss-20b-GGUF

How Much VRAM Does GPT OSS 20B GGUF Need?

Select a quantization to see compatible GPUs below.

Quantization	Bits	VRAM	+ Context	File Size	Quality
Q2_K	3.40	8.9 GB	13.3 GB	8.50 GB	2-bit quantization with K-quant improvements
Q3_K_S	3.50	9.1 GB	13.6 GB	8.75 GB	3-bit small quantization
Q3_K_M	3.90	10.1 GB	14.6 GB	9.75 GB	3-bit medium quantization
Q4_0	4.00	10.4 GB	14.8 GB	10.00 GB	4-bit legacy quantization
Q4_1	4.50	11.6 GB	16.1 GB	11.25 GB	4-bit legacy quantization with offset
Q4_K_S	4.50	11.6 GB	16.1 GB	11.25 GB	4-bit small quantization
Q4_K_M	4.80	12.4 GB	16.8 GB	12.00 GB	4-bit medium quantization — most popular sweet spot
Q5_K_S	5.50	14.1 GB	18.6 GB	13.75 GB	5-bit small quantization
Q5_K_M	5.70	14.6 GB	19.1 GB	14.25 GB	5-bit medium quantization — good quality/size tradeoff
Q6_K	6.60	16.9 GB	21.3 GB	16.50 GB	6-bit quantization, very good quality
Q8_0	8.00	20.4 GB	24.8 GB	20.00 GB	8-bit quantization, near-lossless

Which GPUs Can Run GPT OSS 20B GGUF?

Q4_K_M · 12.4 GB

Show professional

GPT OSS 20B GGUF (Q4_K_M) requires 12.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 17+ GB is recommended. Using the full 131K context window can add up to 4.5 GB, bringing total usage to 16.8 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.

Runs great

— Plenty of headroom

NVIDIA GeForce RTX 5090~94 tok/s NVIDIA GeForce RTX 3090 Ti~53 tok/s NVIDIA GeForce RTX 4090~53 tok/s NVIDIA GeForce RTX 3090~49 tok/s AMD Radeon RX 7900 XTX~43 tok/s AMD Radeon RX 7900 XT~36 tok/s

Decent

— Enough VRAM, may be tight

NVIDIA GeForce RTX 5080~50 tok/s NVIDIA GeForce RTX 5070 Ti~47 tok/s NVIDIA GeForce RTX 4080 SUPER~39 tok/s NVIDIA GeForce RTX 4080~38 tok/s NVIDIA GeForce RTX 4070 Ti SUPER~35 tok/s AMD Radeon RX 7800 XT~28 tok/s AMD Radeon RX 6800~23 tok/s AMD Radeon RX 6800 XT~23 tok/s AMD Radeon RX 6900 XT~23 tok/s Intel Arc A770 16GB~23 tok/s NVIDIA GeForce RTX 4060 Ti 16GB~15 tok/s

Which Devices Can Run GPT OSS 20B GGUF?

Q4_K_M · 12.4 GB

27 devices with unified memory can run GPT OSS 20B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).

Runs great

— Plenty of headroom

Decent

— Enough memory, may be tight

Mac Mini M4 (16 GB)~6 tok/s MacBook Air 13" M4 (16 GB)~6 tok/s MacBook Air 15" M4 (16 GB)~6 tok/s MacBook Pro 14" M4 (16 GB)~6 tok/s iPad Pro M4 13" (16 GB)~6 tok/s MacBook Air 13" M3 (16 GB)~5 tok/s

Related Models

Frequently Asked Questions

How much VRAM does GPT OSS 20B GGUF need?: GPT OSS 20B GGUF requires 12.4 GB of VRAM at Q4_K_M, or 20.4 GB at Q8_0. Full 131K context adds up to 4.5 GB (16.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20B × 4.8 bits ÷ 8 = 12 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M
12.4 GB
Q4_K_M + full context
16.8 GB
Learn more about VRAM estimation →
What's the best quantization for GPT OSS 20B GGUF?: For GPT OSS 20B GGUF, Q4_K_M (12.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (14.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 8.9 GB.
VRAM requirement by quantization
Q2_K
8.9 GB~75%
Q4_0
10.4 GB~85%
Q4_K_S
11.6 GB~88%
Q4_K_M ★
12.4 GB~89%
Q5_K_M
14.6 GB~92%
Q8_0
20.4 GB~99%
★ Recommended — best balance of quality and VRAM usage.
Learn more about quantization →
Can I run GPT OSS 20B GGUF on a Mac?: GPT OSS 20B GGUF requires at least 8.9 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 20B GGUF locally?: Yes — GPT OSS 20B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 12.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT OSS 20B GGUF?: At Q4_K_M, GPT OSS 20B GGUF can reach ~236 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~53 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 ÷ 12.4 × 0.55 = ~236 tok/s
Estimated speed at Q4_K_M (12.4 GB)
AMD Instinct MI300X
~236 tok/s
NVIDIA GeForce RTX 4090
~53 tok/s
NVIDIA H100 SXM
~176 tok/s
AMD Instinct MI250X
~146 tok/s
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
Learn more about tok/s estimation →
What's the download size of 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 (Q2_K) is 8.50 GB.