Qwen3 4B Abliterated — Hardware Requirements & GPU Compatibility
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555 downloads
40 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen
- Parameters
- 4.0B
- Release Date
- 2025-06-19
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 4B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.9 GB | — | 1.71 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.9 GB | — | 1.76 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.2 GB | — | 1.96 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.2 GB | — | 2.01 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.6 GB | — | 2.41 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.1 GB | — | 2.87 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.6 GB | — | 3.32 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.4 GB | — | 4.02 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 4B Abliterated?
Q4_K_M · 2.6 GBQwen3 4B Abliterated (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 4B Abliterated?
Q4_K_M · 2.6 GB33 devices with unified memory can run Qwen3 4B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 4B Abliterated need?
Qwen3 4B Abliterated requires 2.6 GB of VRAM at Q4_K_M, or 4.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.6 GB- What's the best quantization for Qwen3 4B Abliterated?
For Qwen3 4B Abliterated, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (2.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.2 GB.
VRAM requirement by quantization
IQ2_XXS1.2 GB~53%Q3_K_S1.9 GB~77%IQ4_NL2.5 GB~88%Q4_K_M ★2.6 GB~89%Q5_02.8 GB~90%Q8_04.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 4B Abliterated on a Mac?
Qwen3 4B Abliterated requires at least 1.2 GB at IQ2_XXS, 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 4B Abliterated locally?
Yes — Qwen3 4B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 2.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 4B Abliterated?
At Q4_K_M, Qwen3 4B Abliterated can reach ~1100 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~247 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 ÷ 2.6 × 0.55 = ~1100 tok/s
Estimated speed at Q4_K_M (2.6 GB)
AMD Instinct MI300X~1100 tok/sNVIDIA GeForce RTX 4090~247 tok/sNVIDIA H100 SXM~822 tok/sAMD Instinct MI250X~680 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 4B Abliterated?
At Q4_K_M, the download is about 2.41 GB. The full-precision Q8_0 version is 4.02 GB. The smallest option (IQ2_XXS) is 1.11 GB.
- Which GPUs can run Qwen3 4B Abliterated?
35 consumer GPUs can run Qwen3 4B Abliterated at Q4_K_M (2.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 4B Abliterated?
33 devices with unified memory can run Qwen3 4B Abliterated at Q4_K_M (2.6 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.