Qwen3 14B Abliterated — Hardware Requirements & GPU Compatibility
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Qwen3 14B Abliterated is a 14.8B-parameter open language model from huihui-ai in the Qwen 3 family. At Q4_K_M it needs about 9.75 GB of VRAM — see which GPUs and Macs can run it below.
340 downloads
45 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 3
- Parameters
- 14.8B
- Release Date
- 2025-04-30
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 14B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 6.9 GB | — | 6.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 7.1 GB | — | 6.46 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.9 GB | — | 7.20 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 8.1 GB | — | 7.38 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 9.8 GB | — | 8.86 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 11.6 GB | — | 10.52 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 13.4 GB | — | 12.18 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 16.3 GB | — | 14.77 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 14B Abliterated?
Q4_K_M · 9.8 GBQwen3 14B Abliterated (Q4_K_M) requires 9.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3 14B Abliterated?
Q4_K_M · 9.8 GB49 devices with unified memory can run Qwen3 14B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3 14B Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3 14B Abliterated need?
Qwen3 14B Abliterated requires 9.8 GB of VRAM at Q4_K_M, or 32.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 4.8 bits ÷ 8 = 8.9 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M9.8 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 14B Abliterated?
Yes, at Q8_0 (16.3 GB) or lower. Higher quantizations like BF16 (32.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 14B Abliterated?
For Qwen3 14B Abliterated, Q4_K_M (9.8 GB) offers the best balance of quality and VRAM usage. Q4_K_L (9.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 4.9 GB.
VRAM requirement by quantization
IQ2_XS4.9 GBQ3_K_S7.1 GBIQ4_XS8.7 GBQ4_K_M ★9.8 GBQ5_K_S11.2 GBBF1632.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 14B Abliterated on a Mac?
Qwen3 14B Abliterated requires at least 4.9 GB at IQ2_XS, 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 14B Abliterated locally?
Yes — Qwen3 14B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 9.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 14B Abliterated?
At Q4_K_M, Qwen3 14B Abliterated can reach ~492 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~67 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 ÷ 9.8 × 0.65 = ~533 tok/s
Estimated speed at Q4_K_M (9.8 GB)
~533 tok/s~67 tok/s~533 tok/s~492 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 14B Abliterated?
At Q4_K_M, the download is about 8.86 GB. The full-precision BF16 version is 29.54 GB. The smallest option (IQ2_XS) is 4.43 GB.
- Which GPUs can run Qwen3 14B Abliterated?
40 consumer GPUs can run Qwen3 14B Abliterated at Q4_K_M (9.8 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 14B Abliterated?
52 devices with unified memory can run Qwen3 14B Abliterated at Q4_K_M (9.8 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.