Huihui Qwen3 8B Abliterated v2 — Hardware Requirements & GPU Compatibility
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25.3K downloads
36 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-06-18
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Huihui Qwen3 8B Abliterated v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 5.5 GB | 11.3 GB | 4.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 5.7 GB | 11.5 GB | 5.12 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 6.4 GB | 12.2 GB | 5.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.4 GB | 13.1 GB | 6.76 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | 14.5 GB | 8.19 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Huihui Qwen3 8B Abliterated v2?
Q4_K_M · 5.5 GBHuihui Qwen3 8B Abliterated v2 (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 41K context window can add up to 5.7 GB, bringing total usage to 11.3 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Huihui Qwen3 8B Abliterated v2?
Q4_K_M · 5.5 GB33 devices with unified memory can run Huihui Qwen3 8B Abliterated v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Huihui Qwen3 8B Abliterated v2 need?
Huihui Qwen3 8B Abliterated v2 requires 5.5 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0. Full 41K context adds up to 5.7 GB (11.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.4 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M5.5 GBQ4_K_M + full context11.3 GB- What's the best quantization for Huihui Qwen3 8B Abliterated v2?
For Huihui Qwen3 8B Abliterated v2, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.7 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★5.5 GB~89%Q5_05.7 GB~90%Q5_K_M6.4 GB~92%Q6_K7.4 GB~95%Q8_08.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui Qwen3 8B Abliterated v2 on a Mac?
Huihui Qwen3 8B Abliterated v2 requires at least 5.5 GB at Q4_K_M, 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 8B Abliterated v2 locally?
Yes — Huihui Qwen3 8B Abliterated v2 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui Qwen3 8B Abliterated v2?
At Q4_K_M, Huihui Qwen3 8B Abliterated v2 can reach ~528 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~119 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 ÷ 5.5 × 0.55 = ~528 tok/s
Estimated speed at Q4_K_M (5.5 GB)
AMD Instinct MI300X~528 tok/sNVIDIA GeForce RTX 4090~119 tok/sNVIDIA H100 SXM~395 tok/sAMD Instinct MI250X~327 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui Qwen3 8B Abliterated v2?
At Q4_K_M, the download is about 4.91 GB. The full-precision Q8_0 version is 8.19 GB.
- Which GPUs can run Huihui Qwen3 8B Abliterated v2?
35 consumer GPUs can run Huihui Qwen3 8B Abliterated v2 at Q4_K_M (5.5 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 8B Abliterated v2?
33 devices with unified memory can run Huihui Qwen3 8B Abliterated v2 at Q4_K_M (5.5 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.