Qwen3 8B Abliterated — Hardware Requirements & GPU Compatibility
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Qwen3 8B Abliterated is a 8.2B-parameter open language model from huihui-ai in the Qwen 3 family. At Q4_K_M it needs about 5.41 GB of VRAM — see which GPUs and Macs can run it below.
966 downloads
47 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 3
- Parameters
- 8.2B
- Release Date
- 2025-04-30
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 8B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 3.8 GB | — | 3.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.4 GB | — | 3.99 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.4 GB | — | 4.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.4 GB | — | 5.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.4 GB | — | 6.76 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 9.0 GB | — | 8.19 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 18.0 GB | — | 16.38 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 Qwen3 8B Abliterated?
Q4_K_M · 5.4 GBQwen3 8B Abliterated (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3 8B Abliterated?
Q4_K_M · 5.4 GB58 devices with unified memory can run Qwen3 8B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 8B Abliterated need?
Qwen3 8B Abliterated requires 5.4 GB of VRAM at Q4_K_M, or 18.0 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.4 GB- What's the best quantization for Qwen3 8B Abliterated?
For Qwen3 8B Abliterated, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.8 GB.
VRAM requirement by quantization
Q2_K3.8 GBQ4_K_M ★5.4 GBQ5_K_M6.4 GBQ6_K7.4 GBQ8_09.0 GBBF1618.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 8B Abliterated on a Mac?
Qwen3 8B Abliterated requires at least 3.8 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 Qwen3 8B Abliterated locally?
Yes — Qwen3 8B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 8B Abliterated?
At Q4_K_M, Qwen3 8B Abliterated can reach ~813 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~121 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 ÷ 5.4 × 0.65 = ~961 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~961 tok/s~121 tok/s~961 tok/s~813 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 8B Abliterated?
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 Qwen3 8B Abliterated?
50 consumer GPUs can run Qwen3 8B Abliterated at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 8B Abliterated?
59 devices with unified memory can run Qwen3 8B Abliterated at Q4_K_M (5.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.