OpenAI
GPT-OSS 20B (21B parameters) requires approximately 17.1 GB of VRAM with Q4_K_M quantization. As a Mixture of Experts model with 3.5999999046325684B active parameters, it uses less memory than its total parameter count suggests. For the best balance of quality and speed, we recommend hardware with at least 20 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run GPT-OSS 20B on your machine.
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ollama run gpt-ossQuick specs
About this model
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Inference speed
Estimated decode speed (tokens/sec) for GPT-OSS 20B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~231 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 230.5 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 147.1 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 132.7 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 125.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 106.9 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 89.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 84.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 68.2 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 66.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 66.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 46.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 42.2 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 40.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 24.2 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 15.2 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 5.7 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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Quantization
How much VRAM GPT-OSS 20B (21B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q4_K_M uses ~12.8 GB — about 43% less VRAM than Q8_0, at a small quality cost.
| Quant | Bits | VRAM (weights) | Quality | Fits 24 GB? |
|---|---|---|---|---|
| Q2_K | 2 | 8.2 GB | Low | Fits |
| Q3_K_S | 3 | 10.3 GB | Low | Fits |
| NVFP4 | 4 | 11.8 GB | Medium | Fits |
| Q4_K_Mrecommended | 4 | 12.8 GB | Medium | Fits |
| Q5_K_M | 5 | 15.1 GB | High | Tight |
| Q6_K | 6 | 17.2 GB | High | Offloads |
| Q8_0 | 8 | 22.5 GB | Very High | Heavy offload |
| F16 | 16 | 43.1 GB | Maximum | Too big |
VRAM shown is quantized weights only; add ~1–3 GB runtime overhead plus KV cache for your context length. Lower quants trade quality for memory — Q4_K_M is the usual sweet spot; Q2/Q3 only when you must fit a bigger model.
Quality benchmarks
Coding
Reasoning
Source: official · 2025-08-15
Hardware compatibility
Computing compatibility...
Memory breakdown
Frequently asked questions
GPT-OSS 20B (21B parameters) requires approximately 17.1 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.
Yes, Intel Arc Pro B60 24GB can run GPT-OSS 20B with a compatibility score of 93/100. It provides 24 GB of memory and achieves approximately 47.3 tokens per second.
The recommended quantization for GPT-OSS 20B is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.
The top recommended hardware for GPT-OSS 20B: RTX 3090 24GB (score: 95/100), RTX 3090 Ti 24GB (score: 95/100), RTX 4090 24GB (score: 95/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.
Yes, GPT-OSS 20B is well-suited for chat as well as reasoning, code, agentic. It was designed with these use cases in mind.
See also
