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.
Runs great on a 24 GB GPU
Runs from 8 GB (IQ2_XS · 16K)Best on 24 GB (Q4_K_M · ~67 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~14 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3 14B Abliterated
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/bartowski/huihui-ai_Qwen3-14B-abliterated-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 11.2 GB free · 16K context
Download: huihui-ai_Qwen3-14B-abliterated-Q4_K_M.gguf9.00 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~62 tok/s
- generation speed
- Cost per 1M output tokens
- $0.76
- per 1M output tokens
How Much VRAM Does Qwen3 14B Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.9 GB | 13.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~95 tok/s | |
| 7.1 GB | 13.6 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~92 tok/s | |
| 7.9 GB | 14.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~83 tok/s | |
| 8.1 GB | 14.7 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~81 tok/s | |
| 9.8 GB | 16.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~67 tok/s | |
| 11.6 GB | 18.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~57 tok/s | |
| 13.4 GB | 19.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~49 tok/s | |
| 16.3 GB | 22.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~40 tok/s |
Selected Q4_K_M
4-bit medium quantization12.8GBat 16K context
- Weights
- 9.0 GB
- KV cache
- 3.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 11.2 GB free · ~67 tok/s
≥50 tok/s needs ≥749 GB/s · RTX 5090 ~120, RTX 3090 Ti ~67
Which Hardware 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. 150 devices with unified memory can run Qwen3 14B Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
16–18 GB
16–18 GB tier →fits · 3.2 GB free · up to 16K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~64Simulate output on RTX 5080RTX 5070 Ti~60Simulate output on RTX 5070 TiRTX 4080 SUPER~49Simulate output on RTX 4080 SUPERM516 GB~11Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~11Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~11Simulate output on iPad Pro M5 13" (16 GB)Orin NX~7Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 7.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~49Simulate output on RX 7900 XT32–40 GB
fits · 19.2 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~120Simulate output on RTX 5090M5 Max36 GB~33Simulate output on Mac Studio M5 Max (36 GB)M532 GB~11Simulate output on MacBook Air 13" M5 (32 GB)Orin~14Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~9Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3 14B Abliterated
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Apriel 1.6 15B ThinkerServiceNow-AI | — | 13.2 GB | ~67 | same size |
| Phi 4 ReasoningMicrosoft | — | 12.5 GB | ~69 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Qwen1.5 14BAlibaba | — | 22.2 GB | ~63 | same size |
| JiRackUltra 14BCMSManhattan | — | 12.4 GB | ~69 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
Specifications
- Publisher
- huihui-ai
- Family
- Qwen 3
- Parameters
- 14.8B
- Release Date
- 2025-04-30
- License
- Apache 2.0
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 14B Abliterated on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3 14B Abliterated. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 9.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3 14B Abliterated locally?
Yes — Qwen3 14B Abliterated can run locally on consumer hardware. 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 ÷ 9.75 × 0.65 = ~533 tok/s
Estimated speed at Q4_K_M (9.8 GB)
Real-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. 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?
150 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, 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.