GPT OSS 20B — Hardware Requirements & GPU Compatibility
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GPT-OSS 20B is one of OpenAI's first open-source model releases, marking a historic shift in the company's approach to open weights. At 21.5 billion parameters it delivers strong general-purpose chat and reasoning capabilities informed by the research behind the GPT family, making it a compelling option for users who want OpenAI-grade quality in a locally deployable package. The model runs comfortably on a single high-end consumer GPU such as an RTX 4090 at 4-bit quantization, or on workstation cards with 24 GB or more of VRAM at higher precision. It occupies a practical middle ground between lightweight 7B models and resource-heavy 70B+ offerings.
7.4M downloads
4.5K likesSpecifications
- Publisher
- OpenAI
- Family
- GPT-OSS
- Parameters
- 21.5B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-08-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GPT OSS 20B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 6.3 GB | 10.8 GB | 5.92 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_XS | 2.40 | 6.8 GB | 11.3 GB | 6.45 GB | Importance-weighted 2-bit, extra small |
| IQ2_S | 2.50 | 7.1 GB | 11.6 GB | 6.72 GB | Importance-weighted 2-bit, small |
| IQ2_M | 2.70 | 7.6 GB | 12.1 GB | 7.26 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 8.7 GB | 13.2 GB | 8.34 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 9.2 GB | 13.7 GB | 8.87 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 9.5 GB | 14.0 GB | 9.14 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 9.8 GB | 14.2 GB | 9.41 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 10.1 GB | 14.5 GB | 9.68 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 10.9 GB | 15.3 GB | 10.49 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 11.1 GB | 15.6 GB | 10.76 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 11.4 GB | 15.8 GB | 11.02 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 11.9 GB | 16.4 GB | 11.56 GB | Importance-weighted 4-bit, compact |
| Q4_1 | 4.50 | 12.5 GB | 16.9 GB | 12.10 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 12.5 GB | 16.9 GB | 12.10 GB | 4-bit small quantization |
| IQ4_NL | 4.50 | 12.5 GB | 16.9 GB | 12.10 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 13.3 GB | 17.7 GB | 12.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 13.6 GB | 18.0 GB | 13.18 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 15.2 GB | 19.6 GB | 14.79 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 15.7 GB | 20.2 GB | 15.33 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 16.0 GB | 20.4 GB | 15.60 GB | 5-bit large quantization |
| Q6_K | 6.60 | 18.1 GB | 22.6 GB | 17.75 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 21.9 GB | 26.3 GB | 21.51 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run GPT OSS 20B?
Q4_K_M · 13.3 GBGPT OSS 20B (Q4_K_M) requires 13.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 18+ GB is recommended. Using the full 131K context window can add up to 4.5 GB, bringing total usage to 17.7 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
Decent
— Enough VRAM, may be tightWhich Devices Can Run GPT OSS 20B?
Q4_K_M · 13.3 GB27 devices with unified memory can run GPT OSS 20B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Derivatives (10)
Frequently Asked Questions
- How much VRAM does GPT OSS 20B need?
GPT OSS 20B requires 13.3 GB of VRAM at Q4_K_M, or 21.9 GB at Q8_0. Full 131K context adds up to 4.5 GB (17.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 21.5B × 4.8 bits ÷ 8 = 12.9 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_M13.3 GBQ4_K_M + full context17.7 GB- What's the best quantization for GPT OSS 20B?
For GPT OSS 20B, Q4_K_M (13.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (13.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 6.3 GB.
VRAM requirement by quantization
IQ2_XXS6.3 GB~53%Q2_K9.5 GB~75%Q3_K_L11.4 GB~86%Q4_K_M ★13.3 GB~89%Q4_K_L13.6 GB~90%Q8_021.9 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run GPT OSS 20B on a Mac?
GPT OSS 20B requires at least 6.3 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 GPT OSS 20B locally?
Yes — GPT OSS 20B can run locally on consumer hardware. At Q4_K_M quantization it needs 13.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT OSS 20B?
At Q4_K_M, GPT OSS 20B can reach ~220 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~49 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 ÷ 13.3 × 0.55 = ~220 tok/s
Estimated speed at Q4_K_M (13.3 GB)
AMD Instinct MI300X~220 tok/sNVIDIA GeForce RTX 4090~49 tok/sNVIDIA H100 SXM~164 tok/sAMD Instinct MI250X~136 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 20B?
At Q4_K_M, the download is about 12.91 GB. The full-precision Q8_0 version is 21.51 GB. The smallest option (IQ2_XXS) is 5.92 GB.