GPT OSS 20B — Hardware Requirements & GPU Compatibility
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GPT OSS 20B is a 20B-parameter open language model from tripolskypetr in the GPT-OSS family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 12.37 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_1 · 16K)Best on 24 GB (Q4_K_M · ~227 tok/s · 128K)Macs from 32 GB (~35 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT OSS 20B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve tripolskypetr/gpt-oss-20b --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on Tesla V100 PCIe 16GB (3 GB headroom) · Q4_K_M
- Generation speed
- ~209 tok/s
- generation speed
- Cost per 1M output tokens
- $0.12
- per 1M output tokens
How Much VRAM Does GPT OSS 20B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 8.9 GB | 9.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~283 tok/s | |
| 9.1 GB | 9.6 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~278 tok/s | |
| 10.1 GB | 10.6 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~260 tok/s | |
| 10.4 GB | 10.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~256 tok/s | |
| 12.4 GB | 12.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~227 tok/s | |
| 14.6 GB | 15.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~201 tok/s | |
| 16.9 GB | 17.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~181 tok/s | |
| 20.4 GB | 20.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~156 tok/s |
Selected Q4_K_M
4-bit medium quantization12.6GBat 16K context
- Weights
- 12.0 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 11.4 GB free · ~227 tok/s
≥50 tok/s needs ≥167 GB/s · RTX 5090 ~327, RTX 3090 Ti ~227
Which Hardware Can Run GPT OSS 20B?
Q4_K_M · 12.4 GBGPT OSS 20B (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 2.2 GB, bringing total usage to 14.6 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080. 154 devices with unified memory can run GPT OSS 20B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M2 Pro (16 GB).
16–18 GB
16–18 GB tier →fits · 3.4 GB free · up to 128K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~219Simulate output on RTX 5080RTX 5070 Ti~208Simulate output on RTX 5070 TiRTX 4080 SUPER~180Simulate output on RTX 4080 SUPERM516 GB~42Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~42Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~35Simulate output on iPad Pro M4 13" (16 GB)Orin NX~32Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 7.4 GB free · up to 128K ctx
1 GPU
RX 7900 XT~111Simulate output on RX 7900 XT32–40 GB
fits · 19.4 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~327Simulate output on RTX 5090M5 Max36 GB~90Simulate output on Mac Studio M5 Max (36 GB)M532 GB~42Simulate output on MacBook Air 13" M5 (32 GB)Orin~60Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~39Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to GPT OSS 20B
GPT-OSS family · 14 models →Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Internlm 20BInternLM | — | 45.3 GB | —needs > 24 GB | same size |
| Tri 21B Thinktrillionlabs | — | 45.1 GB | —needs > 24 GB | same size |
| ERNIE 4.5 21B A3B PTBaidu | — | 13.8 GB | ~254 | same size |
| GPT Neox 20BEleutherAI | — | 13.7 GB | ~48 | same size |
Specifications
- Publisher
- tripolskypetr
- Family
- GPT-OSS
- Parameters
- 20B
- Active parameters
- 3.3B per token
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-08-16
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does GPT OSS 20B need?
GPT OSS 20B requires 12.4 GB of VRAM at Q4_K_M, or 40.4 GB at BF16. Full 131K context adds up to 2.2 GB (14.6 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 2.6 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run GPT OSS 20B?
Yes, at Q8_0 (20.4 GB) or lower. Higher quantizations like BF16 (40.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for GPT OSS 20B?
For GPT OSS 20B, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run GPT OSS 20B on a Mac?
Yes, but only at lower quantizations. The smallest Mac that can run GPT OSS 20B is iPad Pro M5 13" (16 GB) at Q2_K; 136 of the 156 Macs we list can run it at some quantization. For Q4_K_M (12.4 GB) you need a Mac with more unified memory.
Can I run GPT OSS 20B locally?
Yes — GPT OSS 20B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT OSS 20B?
At Q4_K_M, GPT OSS 20B can reach ~192 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~227 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.0 GB active ÷ (8000 × 0.65) = 0.39 ms, plus 24 layers × 0.055 ms = 1.32 ms, so 1000 ÷ 1.71 ms = ~585 tok/s
Estimated speed at Q4_K_M (12.4 GB)
Real-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 12.00 GB. The full-precision BF16 version is 40.00 GB. The smallest option (Q2_K) is 8.50 GB.
Which GPUs can run GPT OSS 20B?
26 consumer GPUs can run GPT OSS 20B at Q4_K_M. Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 8 GPUs have plenty of headroom for comfortable inference.
Which devices can run GPT OSS 20B?
154 devices with unified memory can run GPT OSS 20B at Q4_K_M (12.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.