Qwen3 32B — Hardware Requirements & GPU Compatibility
Chat
Qwen3 32B is the flagship dense model in Alibaba Cloud's Qwen 3 series, with 32 billion parameters. It is instruction-tuned for chat and delivers strong performance across reasoning, coding, mathematics, and multilingual tasks. Qwen3 32B supports a hybrid thinking mode that allows the model to engage in extended chain-of-thought reasoning or respond quickly depending on the task, giving users flexibility between depth and speed. The model requires a GPU with at least 24GB of VRAM for quantized inference, placing it within reach of high-end consumer cards like the RTX 4090. It represents a significant generational improvement over Qwen 2.5 in both instruction following and knowledge breadth. Released under the Apache 2.0 license.
2.7M downloads
700 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 32.8B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-27
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 32B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.6 GB | 20.9 GB | 13.92 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.0 GB | 21.3 GB | 14.33 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 16.6 GB | 23.0 GB | 15.97 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.0 GB | 23.4 GB | 16.38 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.3 GB | 26.7 GB | 19.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 24.0 GB | 30.4 GB | 23.34 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.7 GB | 34.0 GB | 27.03 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33.4 GB | 39.8 GB | 32.76 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 32B?
Q4_K_M · 20.3 GBQwen3 32B (Q4_K_M) requires 20.3 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 6.4 GB, bringing total usage to 26.7 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 Qwen3 32B?
Q4_K_M · 20.3 GB41 devices with unified memory can run Qwen3 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 Qwen3 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 Qwen3 32B need?
Qwen3 32B requires 20.3 GB of VRAM at Q4_K_M, or 66.2 GB at BF16. Full 41K context adds up to 6.4 GB (26.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 4.8 bits ÷ 8 = 19.7 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M20.3 GBQ4_K_M + full context26.7 GB- Can NVIDIA GeForce RTX 4090 run Qwen3 32B?
Yes, at Q5_K_M (24.0 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 32B?
For Qwen3 32B, Q4_K_M (20.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (21.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.7 GB.
VRAM requirement by quantization
IQ2_XXS9.7 GBQ3_K_M16.6 GBQ4_K_S19.1 GBQ4_K_M ★20.3 GBQ5_K_S23.2 GBBF1666.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 32B on a Mac?
Qwen3 32B requires at least 9.7 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 32B locally?
Yes — Qwen3 32B can run locally on consumer hardware. At Q4_K_M quantization it needs 20.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 32B?
At Q4_K_M, Qwen3 32B can reach ~217 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 ÷ 20.3 × 0.65 = ~256 tok/s
Estimated speed at Q4_K_M (20.3 GB)
~256 tok/s~32 tok/s~256 tok/s~217 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 32B?
At Q4_K_M, the download is about 19.66 GB. The full-precision BF16 version is 65.52 GB. The smallest option (IQ2_XXS) is 9.01 GB.
- Which GPUs can run Qwen3 32B?
7 consumer GPUs can run Qwen3 32B at Q4_K_M (20.3 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 32B?
41 devices with unified memory can run Qwen3 32B at Q4_K_M (20.3 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.