GPT OSS 20B NPU2 — Hardware Requirements & GPU Compatibility
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GPT OSS 20B NPU2 is a 20B-parameter open language model from FastFlowLM 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.
411.1K downloads
1 likesSpecifications
- Publisher
- FastFlowLM
- Family
- GPT-OSS
- Parameters
- 20B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-09-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GPT OSS 20B NPU2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 8.9 GB | 13.3 GB | 8.50 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 10.1 GB | 14.6 GB | 9.75 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 12.4 GB | 16.8 GB | 12.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 14.6 GB | 19.1 GB | 14.25 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 16.9 GB | 21.3 GB | 16.50 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 20.4 GB | 24.8 GB | 20.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 40.4 GB | 44.8 GB | 40.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run GPT OSS 20B NPU2?
Q4_K_M · 12.4 GBGPT OSS 20B NPU2 (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. 26 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 NPU2?
Q4_K_M · 12.4 GB48 devices with unified memory can run GPT OSS 20B NPU2, 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 GPT OSS 20B NPU2 need?
GPT OSS 20B NPU2 requires 12.4 GB of VRAM at Q4_K_M, or 40.4 GB at BF16. 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_M12.4 GBQ4_K_M + full context16.8 GB- Can NVIDIA GeForce RTX 4090 run GPT OSS 20B NPU2?
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 NPU2?
For GPT OSS 20B NPU2, Q4_K_M (12.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (14.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 8.9 GB.
VRAM requirement by quantization
Q2_K8.9 GBQ4_K_M ★12.4 GBQ5_K_M14.6 GBQ6_K16.9 GBQ8_020.4 GBBF1640.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GPT OSS 20B NPU2 on a Mac?
GPT OSS 20B NPU2 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 NPU2 locally?
Yes — GPT OSS 20B NPU2 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 NPU2?
At Q4_K_M, GPT OSS 20B NPU2 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 = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 12.4 × 0.65 = ~585 tok/s
Estimated speed at Q4_K_M (12.4 GB)
~585 tok/s~227 tok/s~585 tok/s~511 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 NPU2?
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 NPU2?
26 consumer GPUs can run GPT OSS 20B NPU2 at Q4_K_M (12.4 GB). 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 NPU2?
49 devices with unified memory can run GPT OSS 20B NPU2 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.