QwQ 32B Abliterated — Hardware Requirements & GPU Compatibility
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QwQ 32B Abliterated is a 32.8B-parameter open language model from huihui-ai in the QwQ family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 20.50 GB of VRAM — see which GPUs and Macs can run it below.
107 downloads
108 likesSpecifications
- Publisher
- huihui-ai
- Family
- QwQ
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-03-07
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does QwQ 32B Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.8 GB | 48.6 GB | 13.92 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.2 GB | 49.0 GB | 14.33 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.8 GB | 50.6 GB | 15.97 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.2 GB | 51.0 GB | 16.38 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.5 GB | 54.3 GB | 19.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 24.2 GB | 58 GB | 23.34 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.9 GB | 61.7 GB | 27.03 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33.6 GB | 67.4 GB | 32.76 GB | 8-bit quantization, near-lossless |
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 QwQ 32B Abliterated?
Q4_K_M · 20.5 GBQwQ 32B Abliterated (Q4_K_M) requires 20.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 131K context window can add up to 33.8 GB, bringing total usage to 54.3 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run QwQ 32B Abliterated?
Q4_K_M · 20.5 GB41 devices with unified memory can run QwQ 32B Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download QwQ 32B 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 QwQ 32B Abliterated need?
QwQ 32B Abliterated requires 20.5 GB of VRAM at Q4_K_M, or 66.4 GB at BF16. Full 131K context adds up to 33.8 GB (54.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 34.6 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M20.5 GBQ4_K_M + full context54.3 GB- Can NVIDIA GeForce RTX 4090 run QwQ 32B Abliterated?
Yes, at Q5_K_S (23.4 GB) or lower. Higher quantizations like Q5_K_M (24.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for QwQ 32B Abliterated?
For QwQ 32B Abliterated, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.8 GB.
VRAM requirement by quantization
IQ2_XXS9.8 GBQ2_K14.8 GBIQ4_XS18.4 GBQ4_K_M ★20.5 GBQ4_K_L20.9 GBBF1666.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run QwQ 32B Abliterated on a Mac?
QwQ 32B Abliterated requires at least 9.8 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 QwQ 32B Abliterated locally?
Yes — QwQ 32B Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 20.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is QwQ 32B Abliterated?
At Q4_K_M, QwQ 32B Abliterated can reach ~234 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 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 ÷ 20.5 × 0.65 = ~254 tok/s
Estimated speed at Q4_K_M (20.5 GB)
~254 tok/s~32 tok/s~254 tok/s~234 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of QwQ 32B Abliterated?
At Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.53 GB. The smallest option (IQ2_XXS) is 9.01 GB.
- Which GPUs can run QwQ 32B Abliterated?
7 consumer GPUs can run QwQ 32B Abliterated at Q4_K_M (20.5 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 QwQ 32B Abliterated?
41 devices with unified memory can run QwQ 32B Abliterated at Q4_K_M (20.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.