Huihui GPT OSS 20B BF16 Abliterated — Hardware Requirements & GPU Compatibility
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Huihui GPT OSS 20B BF16 Abliterated is a 20.9B-parameter open language model from huihui-ai in the GPT-OSS family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 12.92 GB of VRAM — see which GPUs and Macs can run it below.
25.5K downloads
217 likesSpecifications
- Publisher
- huihui-ai
- Family
- GPT-OSS
- Parameters
- 20.9B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-08-06
- License
- Apache 2.0
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How Much VRAM Does Huihui GPT OSS 20B BF16 Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 9.3 GB | 13.7 GB | 8.89 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 10.6 GB | 15.0 GB | 10.20 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 12.9 GB | 17.4 GB | 12.55 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 15.3 GB | 19.7 GB | 14.90 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 17.6 GB | 22.1 GB | 17.25 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 21.3 GB | 25.7 GB | 20.91 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 42.2 GB | 46.7 GB | 41.83 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 Huihui GPT OSS 20B BF16 Abliterated?
Q4_K_M · 12.9 GBHuihui GPT OSS 20B BF16 Abliterated (Q4_K_M) requires 12.9 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 17.4 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 Huihui GPT OSS 20B BF16 Abliterated?
Q4_K_M · 12.9 GB48 devices with unified memory can run Huihui GPT OSS 20B BF16 Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomWhere to Download Huihui GPT OSS 20B BF16 Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Huihui GPT OSS 20B BF16 Abliterated need?
Huihui GPT OSS 20B BF16 Abliterated requires 12.9 GB of VRAM at Q4_K_M, or 42.2 GB at BF16. Full 131K context adds up to 4.5 GB (17.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20.9B × 4.8 bits ÷ 8 = 12.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.9 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M12.9 GBQ4_K_M + full context17.4 GB- Can NVIDIA GeForce RTX 4090 run Huihui GPT OSS 20B BF16 Abliterated?
Yes, at Q8_0 (21.3 GB) or lower. Higher quantizations like BF16 (42.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Huihui GPT OSS 20B BF16 Abliterated?
For Huihui GPT OSS 20B BF16 Abliterated, Q4_K_M (12.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (15.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 9.3 GB.
VRAM requirement by quantization
Q2_K9.3 GBQ4_K_M ★12.9 GBQ5_K_M15.3 GBQ6_K17.6 GBQ8_021.3 GBBF1642.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui GPT OSS 20B BF16 Abliterated on a Mac?
Huihui GPT OSS 20B BF16 Abliterated requires at least 9.3 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 Huihui GPT OSS 20B BF16 Abliterated locally?
Yes — Huihui GPT OSS 20B BF16 Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 12.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui GPT OSS 20B BF16 Abliterated?
At Q4_K_M, Huihui GPT OSS 20B BF16 Abliterated can reach ~341 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~51 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 ÷ 12.9 × 0.65 = ~403 tok/s
Estimated speed at Q4_K_M (12.9 GB)
~403 tok/s~51 tok/s~403 tok/s~341 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui GPT OSS 20B BF16 Abliterated?
At Q4_K_M, the download is about 12.55 GB. The full-precision BF16 version is 41.83 GB. The smallest option (Q2_K) is 8.89 GB.
- Which GPUs can run Huihui GPT OSS 20B BF16 Abliterated?
26 consumer GPUs can run Huihui GPT OSS 20B BF16 Abliterated at Q4_K_M (12.9 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 Huihui GPT OSS 20B BF16 Abliterated?
49 devices with unified memory can run Huihui GPT OSS 20B BF16 Abliterated at Q4_K_M (12.9 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.