Qwen2.5 Coder 14B Instruct GGUF — Hardware Requirements & GPU Compatibility
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Qwen2.5 Coder 14B Instruct is a mid-range code-specialized model from Alibaba, released in official GGUF format. Its 14 billion parameters give it a meaningful quality advantage over the 7B coding variant, producing more accurate completions, better handling of complex logic, and stronger performance on multi-file refactoring tasks. The 14B size is well suited to users with a 12-to-16 GB GPU who want the best coding capability their hardware can support. Quantized GGUF options make it feasible on cards like the RTX 4070 or RTX 3090, delivering a strong local coding experience without resorting to cloud APIs.
51.3K downloads
98 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 14B
- Release Date
- 2024-11-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 Coder 14B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 6.5 GB | — | 5.95 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 7.5 GB | — | 6.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 7.7 GB | — | 7.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 9.2 GB | — | 8.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 9.6 GB | — | 8.75 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 11.0 GB | — | 9.97 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 12.7 GB | — | 11.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 15.4 GB | — | 14.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 Coder 14B Instruct GGUF?
Q4_K_M · 9.2 GBQwen2.5 Coder 14B Instruct GGUF (Q4_K_M) requires 9.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen2.5 Coder 14B Instruct GGUF?
Q4_K_M · 9.2 GB27 devices with unified memory can run Qwen2.5 Coder 14B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 Coder 14B Instruct GGUF need?
Qwen2.5 Coder 14B Instruct GGUF requires 9.2 GB of VRAM at Q4_K_M, or 15.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 14B × 4.8 bits ÷ 8 = 8.4 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M9.2 GB- What's the best quantization for Qwen2.5 Coder 14B Instruct GGUF?
For Qwen2.5 Coder 14B Instruct GGUF, Q4_K_M (9.2 GB) offers the best balance of quality and VRAM usage. Q5_0 (9.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.5 GB.
VRAM requirement by quantization
Q2_K6.5 GB~75%Q4_07.7 GB~85%Q4_K_M ★9.2 GB~89%Q5_09.6 GB~90%Q5_K_M11.0 GB~92%Q8_015.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 Coder 14B Instruct GGUF on a Mac?
Qwen2.5 Coder 14B Instruct GGUF requires at least 6.5 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 14B Instruct GGUF locally?
Yes — Qwen2.5 Coder 14B Instruct GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 9.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 Coder 14B Instruct GGUF?
At Q4_K_M, Qwen2.5 Coder 14B Instruct GGUF can reach ~316 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~71 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 ÷ 9.2 × 0.55 = ~316 tok/s
Estimated speed at Q4_K_M (9.2 GB)
AMD Instinct MI300X~316 tok/sNVIDIA GeForce RTX 4090~71 tok/sNVIDIA H100 SXM~236 tok/sAMD Instinct MI250X~195 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 14B Instruct GGUF?
At Q4_K_M, the download is about 8.40 GB. The full-precision Q8_0 version is 14.00 GB. The smallest option (Q2_K) is 5.95 GB.