Qwen2.5 32B — Hardware Requirements & GPU Compatibility
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Qwen2.5 32B is a 32.8B-parameter open language model from Alibaba in the Qwen 2.5 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.
111.2K downloads
177 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-09-20
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 32B 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 |
Which GPUs Can Run Qwen2.5 32B?
Q4_K_M · 20.5 GBQwen2.5 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 131K context window can add up to 33.8 GB, bringing total usage to 54.3 GB. 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 Qwen2.5 32B?
Q4_K_M · 20.5 GB21 devices with unified memory can run Qwen2.5 32B, 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 Qwen2.5 32B need?
Qwen2.5 32B requires 20.5 GB of VRAM at Q4_K_M, or 33.6 GB at Q8_0. 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 Qwen2.5 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 Qwen2.5 32B?
For Qwen2.5 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 IQ3_XS at 14.3 GB.
VRAM requirement by quantization
IQ3_XS14.3 GBQ3_K_M16.8 GBQ4_K_S19.3 GBQ4_K_M ★20.5 GBQ5_123.4 GBQ8_033.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 32B on a Mac?
Qwen2.5 32B requires at least 14.3 GB at IQ3_XS, 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 Qwen2.5 32B locally?
Yes — Qwen2.5 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 Qwen2.5 32B?
At Q4_K_M, Qwen2.5 32B can reach ~142 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 20.5 × 0.55 = ~142 tok/s
Estimated speed at Q4_K_M (20.5 GB)
~142 tok/s~32 tok/s~106 tok/s~88 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2.5 32B?
At Q4_K_M, the download is about 19.66 GB. The full-precision Q8_0 version is 32.76 GB. The smallest option (IQ3_XS) is 13.52 GB.
- Which GPUs can run Qwen2.5 32B?
5 consumer GPUs can run Qwen2.5 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 Qwen2.5 32B?
21 devices with unified memory can run Qwen2.5 32B at Q4_K_M (20.5 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.