QwQ 32B — Hardware Requirements & GPU Compatibility
ChatReasoning
QwQ 32B is a 32-billion parameter reasoning-focused model from Alibaba Cloud's Qwen family. Unlike standard chat models, QwQ is specifically optimized for step-by-step logical reasoning, complex problem solving, and mathematical tasks. It employs extended chain-of-thought processing, generating detailed internal reasoning before producing final answers, which significantly improves accuracy on challenging analytical problems. The model requires a GPU with at least 24GB of VRAM for quantized inference and delivers reasoning performance competitive with much larger models. It is particularly well suited for users who need strong analytical capabilities for math, science, coding logic, and multi-step problem solving. Released under the Apache 2.0 license.
50.0K downloads
2.9K likesSpecifications
- Publisher
- Alibaba
- Family
- QwQ
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-03-05
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does QwQ 32B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.8 GB | 25.0 GB | 13.92 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.2 GB | 25.4 GB | 14.33 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.8 GB | 27.0 GB | 15.97 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.2 GB | 27.4 GB | 16.38 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.5 GB | 30.7 GB | 19.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 24.2 GB | 34.4 GB | 23.34 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.9 GB | 38.1 GB | 27.03 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33.6 GB | 43.8 GB | 32.76 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run QwQ 32B?
Q4_K_M · 20.5 GBQwQ 32B (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 41K context window can add up to 10.2 GB, bringing total usage to 30.7 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?
Q4_K_M · 20.5 GB41 devices with unified memory can run QwQ 32B, 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
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does QwQ 32B need?
QwQ 32B requires 20.5 GB of VRAM at Q4_K_M, or 66.4 GB at BF16. Full 41K context adds up to 10.2 GB (30.7 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 ≈ 11 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M20.5 GBQ4_K_M + full context30.7 GB- Can NVIDIA GeForce RTX 4090 run QwQ 32B?
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?
For QwQ 32B, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.3 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 GBQ3_K_M16.8 GBQ4_K_S19.3 GBQ4_K_M ★20.5 GBQ5_K_S23.4 GBBF1666.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run QwQ 32B on a Mac?
QwQ 32B 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 locally?
Yes — QwQ 32B 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?
At Q4_K_M, QwQ 32B can reach ~215 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~215 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?
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?
7 consumer GPUs can run QwQ 32B 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?
41 devices with unified memory can run QwQ 32B 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.