Qwen2.5 Coder 32B Instruct GGUF — Hardware Requirements & GPU Compatibility
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Qwen2.5 Coder 32B Instruct is the flagship code-specialized model in Alibaba's Qwen2.5 lineup, released in official GGUF format. With 32 billion parameters trained heavily on programming data, it delivers strong performance across code generation, refactoring, debugging, and technical explanation, rivaling much larger proprietary coding assistants on many benchmarks. Running the 32B model locally requires a higher-end setup, typically 24 GB or more of VRAM at moderate quantization levels, but the payoff is a highly capable offline coding companion with no API costs or data-privacy concerns. Lower quantizations can bring it within reach of 16 GB cards with some quality trade-off.
165.9K downloads
189 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 32B
- Release Date
- 2025-01-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 Coder 32B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 15.0 GB | — | 13.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 17.2 GB | — | 15.60 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 17.6 GB | — | 16.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 21.1 GB | — | 19.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 22 GB | — | 20.00 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 25.1 GB | — | 22.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 29.0 GB | — | 26.40 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 35.2 GB | — | 32.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 Coder 32B Instruct GGUF?
Q4_K_M · 21.1 GBQwen2.5 Coder 32B Instruct GGUF (Q4_K_M) requires 21.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ 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 Qwen2.5 Coder 32B Instruct GGUF?
Q4_K_M · 21.1 GB21 devices with unified memory can run Qwen2.5 Coder 32B Instruct GGUF, 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 Coder 32B Instruct GGUF need?
Qwen2.5 Coder 32B Instruct GGUF requires 21.1 GB of VRAM at Q4_K_M, or 35.2 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 32B × 4.8 bits ÷ 8 = 19.2 GB
KV Cache + Overhead ≈ 1.9 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M21.1 GB- Can NVIDIA GeForce RTX 4090 run Qwen2.5 Coder 32B Instruct GGUF?
Yes, at Q5_0 (22 GB) or lower. Higher quantizations like Q5_K_M (25.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen2.5 Coder 32B Instruct GGUF?
For Qwen2.5 Coder 32B Instruct GGUF, Q4_K_M (21.1 GB) offers the best balance of quality and VRAM usage. Q5_0 (22 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 15.0 GB.
VRAM requirement by quantization
Q2_K15.0 GB~75%Q4_017.6 GB~85%Q4_K_M ★21.1 GB~89%Q5_022.0 GB~90%Q5_K_M25.1 GB~92%Q8_035.2 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 Coder 32B Instruct GGUF on a Mac?
Qwen2.5 Coder 32B Instruct GGUF requires at least 15.0 GB at Q2_K, 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 Coder 32B Instruct GGUF locally?
Yes — Qwen2.5 Coder 32B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 21.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 Coder 32B Instruct GGUF?
At Q4_K_M, Qwen2.5 Coder 32B Instruct GGUF can reach ~138 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~31 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 ÷ 21.1 × 0.55 = ~138 tok/s
Estimated speed at Q4_K_M (21.1 GB)
AMD Instinct MI300X~138 tok/sNVIDIA GeForce RTX 4090~31 tok/sNVIDIA H100 SXM~103 tok/sAMD Instinct MI250X~85 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 Coder 32B Instruct GGUF?
At Q4_K_M, the download is about 19.20 GB. The full-precision Q8_0 version is 32.00 GB. The smallest option (Q2_K) is 13.60 GB.