Qwen3 30B A3B Abliterated — Hardware Requirements & GPU Compatibility
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Qwen3 30B A3B Abliterated is a 30.5B-parameter open language model from huihui-ai in the Qwen family. At Q4_K_M it needs about 20.15 GB of VRAM — see which GPUs and Macs can run it below.
307 downloads
61 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen
- Parameters
- 30.5B
- Release Date
- 2025-06-09
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Qwen3 30B A3B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.3 GB | — | 12.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 14.7 GB | — | 13.36 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.4 GB | — | 14.88 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 16.8 GB | — | 15.27 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.1 GB | — | 18.32 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 23.9 GB | — | 21.75 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.7 GB | — | 25.19 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33.6 GB | — | 30.53 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 30B A3B Abliterated?
Q4_K_M · 20.1 GBQwen3 30B A3B Abliterated (Q4_K_M) requires 20.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3 30B A3B Abliterated?
Q4_K_M · 20.1 GB21 devices with unified memory can run Qwen3 30B A3B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 30B A3B Abliterated need?
Qwen3 30B A3B Abliterated requires 20.1 GB of VRAM at Q4_K_M, or 33.6 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 30.5B × 4.8 bits ÷ 8 = 18.3 GB
KV Cache + Overhead ≈ 1.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M20.1 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 30B A3B Abliterated?
Yes, at Q5_K_M (23.9 GB) or lower. Higher quantizations like Q6_K (27.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3 30B A3B Abliterated?
For Qwen3 30B A3B Abliterated, Q4_K_M (20.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (23.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.2 GB.
VRAM requirement by quantization
IQ2_XXS9.2 GBQ3_K_S14.7 GBQ4_118.9 GBQ4_K_M ★20.1 GBQ5_K_S23.1 GBQ8_033.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 30B A3B Abliterated on a Mac?
Qwen3 30B A3B Abliterated requires at least 9.2 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 30B A3B Abliterated locally?
Yes — Qwen3 30B A3B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 20.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 30B A3B Abliterated?
At Q4_K_M, Qwen3 30B A3B Abliterated can reach ~145 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~33 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 ÷ 20.1 × 0.55 = ~145 tok/s
Estimated speed at Q4_K_M (20.1 GB)
~145 tok/s~33 tok/s~108 tok/s~89 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 30B A3B Abliterated?
At Q4_K_M, the download is about 18.32 GB. The full-precision Q8_0 version is 30.53 GB. The smallest option (IQ2_XXS) is 8.40 GB.
- Which GPUs can run Qwen3 30B A3B Abliterated?
5 consumer GPUs can run Qwen3 30B A3B Abliterated at Q4_K_M (20.1 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 30B A3B Abliterated?
21 devices with unified memory can run Qwen3 30B A3B Abliterated at Q4_K_M (20.1 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.