Abhiray·Qwen

Huihui Qwen3.5 9B Abliterated GGUF — Hardware Requirements & GPU Compatibility

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Specifications

Publisher
Abhiray
Family
Qwen
Parameters
9B
Release Date
2026-03-10

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How Much VRAM Does Huihui Qwen3.5 9B Abliterated GGUF Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q4_K_S4.505.6 GB
Q4_K_M4.805.9 GB
Q5_K_S5.506.8 GB
Q5_K_M5.707.0 GB
Q6_K6.608.2 GB
Q8_08.009.9 GB

Which GPUs Can Run Huihui Qwen3.5 9B Abliterated GGUF?

Q4_K_M · 5.9 GB

Huihui Qwen3.5 9B Abliterated GGUF (Q4_K_M) requires 5.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.

Which Devices Can Run Huihui Qwen3.5 9B Abliterated GGUF?

Q4_K_M · 5.9 GB

33 devices with unified memory can run Huihui Qwen3.5 9B Abliterated GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).

Related Models

Frequently Asked Questions

How much VRAM does Huihui Qwen3.5 9B Abliterated GGUF need?

Huihui Qwen3.5 9B Abliterated GGUF requires 5.9 GB of VRAM at Q4_K_M, or 9.9 GB at Q8_0.

VRAM = Weights + KV Cache + Overhead

Weights = 9B × 4.8 bits ÷ 8 = 5.4 GB

KV Cache + Overhead 0.5 GB (at 2K context + ~0.3 GB framework)

VRAM usage by quantization

5.9 GB

Learn more about VRAM estimation →

What's the best quantization for Huihui Qwen3.5 9B Abliterated GGUF?

For Huihui Qwen3.5 9B Abliterated GGUF, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.8 GB) provides better quality if you have the VRAM. The smallest option is Q4_K_S at 5.6 GB.

VRAM requirement by quantization

Q4_K_S
5.6 GB
Q4_K_M
5.9 GB
Q5_K_S
6.8 GB
Q5_K_M
7.0 GB
Q6_K
8.2 GB
Q8_0
9.9 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Huihui Qwen3.5 9B Abliterated GGUF on a Mac?

Huihui Qwen3.5 9B Abliterated GGUF requires at least 5.6 GB at Q4_K_S, 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 Qwen3.5 9B Abliterated GGUF locally?

Yes — Huihui Qwen3.5 9B Abliterated GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 5.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Huihui Qwen3.5 9B Abliterated GGUF?

At Q4_K_M, Huihui Qwen3.5 9B Abliterated GGUF can reach ~491 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~110 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 MI300X5300 ÷ 5.9 × 0.55 = ~491 tok/s

Estimated speed at Q4_K_M (5.9 GB)

~491 tok/s
~110 tok/s
~367 tok/s
~303 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Huihui Qwen3.5 9B Abliterated GGUF?

At Q4_K_M, the download is about 5.40 GB. The full-precision Q8_0 version is 9.00 GB. The smallest option (Q4_K_S) is 5.06 GB.

Which GPUs can run Huihui Qwen3.5 9B Abliterated GGUF?

35 consumer GPUs can run Huihui Qwen3.5 9B Abliterated GGUF at Q4_K_M (5.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.

Which devices can run Huihui Qwen3.5 9B Abliterated GGUF?

33 devices with unified memory can run Huihui Qwen3.5 9B Abliterated GGUF at Q4_K_M (5.9 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.