Huihui Qwen3 8B Abliterated v2 — Hardware Requirements & GPU Compatibility
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Huihui Qwen3 8B Abliterated v2 is a 8.2B-parameter open language model from huihui-ai in the Qwen 3 family. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 5.52 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~119 tok/s · 40K)Macs from 16 GB (Q4_K_M · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Huihui Qwen3 8B Abliterated v2
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Huihui-Qwen3-8B-abliterated-v2-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.4 GB free · 16K context
Download: Huihui-Qwen3-8B-abliterated-v2.Q4_K_M.gguf5.03 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 (10 GB) (2 GB headroom) · Q4_K_M
- Generation speed
- ~90 tok/s
- generation speed
- Cost per 1M output tokens
- $0.34
- per 1M output tokens
How Much VRAM Does Huihui Qwen3 8B Abliterated v2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.1 GB | 8.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~161 tok/s | |
| 4.2 GB | 8.7 GB | 3-bit small — 3-bit small quantization | Fits: 40K context · ~156 tok/s | |
| 4.6 GB | 9.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~143 tok/s | |
| 4.8 GB | 9.3 GB | 3-bit large — 3-bit large quantization | Fits: 40K context · ~137 tok/s | |
| 5.5 GB | 10.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 40K context · ~119 tok/s | |
| 6.4 GB | 11 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 40K context · ~102 tok/s | |
| 7.4 GB | 11.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 40K context · ~89 tok/s | |
| 8.8 GB | 13.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 40K context · ~75 tok/s |
Selected Q4_K_M
4-bit medium quantization7.6GBat 16K context
- Weights
- 4.9 GB
- KV cache
- 2.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.4 GB free · ~119 tok/s
≥50 tok/s needs ≥424 GB/s · RTX 5090 ~211, RTX 3090 Ti ~119
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Huihui Qwen3 8B Abliterated v2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.4 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~90Simulate output on RTX 3080RTX 2080 Ti~73Simulate output on RTX 2080 TiRTX 3070 Ti~72Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.4 GB free · up to 40K ctx
1 GPU
RX 7900 XT~87Simulate output on RX 7900 XT32–40 GB
fits · 24.4 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~211Simulate output on RTX 5090M5 Max36 GB~58Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~24Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Huihui Qwen3 8B Abliterated v2
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
Specifications
- Publisher
- huihui-ai
- Family
- Qwen 3
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-06-18
- License
- Apache 2.0
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 17.0 GB at BF16. 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 6.4 GB (at full 41K context)
VRAM usage by quantization
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_K_S (6.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Huihui Qwen3 8B Abliterated v2 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Huihui Qwen3 8B Abliterated v2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Huihui Qwen3 8B Abliterated v2 locally?
Yes — Huihui Qwen3 8B Abliterated v2 can run locally on consumer hardware. 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 ~870 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 5.52 × 0.65 = ~942 tok/s
Estimated speed at Q4_K_M (5.5 GB)
Real-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 BF16 version is 16.38 GB. The smallest option (Q2_K) is 3.48 GB.
Which GPUs can run Huihui Qwen3 8B Abliterated v2?
52 consumer GPUs can run Huihui Qwen3 8B Abliterated v2 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Huihui Qwen3 8B Abliterated v2?
151 devices with unified memory can run Huihui Qwen3 8B Abliterated v2 at Q4_K_M (5.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.