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 IQ4_NL it needs about 12.14 GB of VRAM — see which GPUs and Macs can run it below.
30.1K downloads
216 likesSpecifications
- Publisher
- huihui-ai
- Family
- GPT-OSS
- Parameters
- 20.9B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2025-09-27
- License
- Apache 2.0
Get Started
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 |
|---|---|---|---|---|---|
| IQ4_NL | 4.50 | 12.1 GB | 16.6 GB | 11.76 GB | Importance-weighted 4-bit, non-linear |
| Q5_1 | 5.50 | 14.8 GB | 19.2 GB | 14.38 GB | 5-bit legacy quantization with offset |
| Q8_0 | 8.00 | 21.3 GB | 25.7 GB | 20.91 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Huihui GPT OSS 20B BF16 Abliterated?
Q8_0 · 21.3 GBHuihui GPT OSS 20B BF16 Abliterated (Q8_0) requires 21.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 131K context window can add up to 4.4 GB, bringing total usage to 25.7 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Huihui GPT OSS 20B BF16 Abliterated?
Q8_0 · 21.3 GB21 devices with unified memory can run Huihui GPT OSS 20B BF16 Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Huihui GPT OSS 20B BF16 Abliterated need?
Huihui GPT OSS 20B BF16 Abliterated requires 12.1 GB of VRAM at IQ4_NL, or 21.3 GB at Q8_0. Full 131K context adds up to 4.4 GB (16.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20.9B × 4.5 bits ÷ 8 = 11.8 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.8 GB (at full 131K context)
VRAM usage by quantization
IQ4_NL12.1 GBIQ4_NL + full context16.6 GB- What's the best quantization for Huihui GPT OSS 20B BF16 Abliterated?
For Huihui GPT OSS 20B BF16 Abliterated, Q5_1 (14.8 GB) offers the best balance of quality and VRAM usage. Q8_0 (21.3 GB) provides better quality if you have the VRAM. The smallest option is IQ4_NL at 12.1 GB.
VRAM requirement by quantization
IQ4_NL12.1 GBQ5_1 ★14.8 GBQ8_021.3 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 12.1 GB at IQ4_NL, 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 IQ4_NL quantization it needs 12.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui GPT OSS 20B BF16 Abliterated?
At IQ4_NL, Huihui GPT OSS 20B BF16 Abliterated can reach ~240 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~54 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 ÷ 12.1 × 0.55 = ~240 tok/s
Estimated speed at IQ4_NL (12.1 GB)
~240 tok/s~54 tok/s~180 tok/s~149 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 IQ4_NL, the download is about 11.76 GB. The full-precision Q8_0 version is 20.91 GB.
- Which GPUs can run Huihui GPT OSS 20B BF16 Abliterated?
17 consumer GPUs can run Huihui GPT OSS 20B BF16 Abliterated at IQ4_NL (12.1 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 6 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Huihui GPT OSS 20B BF16 Abliterated?
27 devices with unified memory can run Huihui GPT OSS 20B BF16 Abliterated at IQ4_NL (12.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.