Qwen 2.5 Coder 14B is a powerful code generation model developed by Alibaba, boasting 14 billion parameters and designed to handle extensive context lengths up to 32,768 tokens. This model excels in generating high-quality, contextually relevant code snippets across various programming languages, making it an invaluable tool for developers looking to automate repetitive coding tasks, generate documentation, or explore new coding ideas. The Apache 2.0 license ensures that users can freely integrate and modify the model for both personal and commercial projects.
In its size class, Qwen 2.5 Coder 14B holds its own, offering a balance between performance and efficiency. While it requires a significant amount of VRAM (8.9–15.1 GB), it manages to deliver robust results without being overly resource-intensive compared to other models of similar size. This makes it a practical choice for developers with mid-range to high-end GPUs. Ideal users include software engineers, data scientists, and researchers who need a reliable code generation tool that can be deployed locally. Realistic hardware requirements include a modern GPU with at least 12 GB of VRAM for smooth operation, ensuring that the model can handle complex tasks without performance bottlenecks.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 8.371 GB | 8.87 GB | 9.37 GB | 85% |
| Q8_0 | 8 | 14.623 GB | 15.12 GB | 15.62 GB | 98% |
Context window & KV cache
Adds 1.25 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 32K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Qwen 2.5 Coder 14B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull qwen2.5-coder:14b - 2
Chat
ollama run qwen2.5-coder:14b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"qwen2.5-coder:14b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Qwen 2.5 Coder 14B on actual hardware.
| GPU | Median tok/s | Reports | Typical setup |
|---|---|---|---|
| RTX 4090 | 52.7 | 1 | Q4_K_M · Ollama · Linux · 8K ctx |
| RTX 3090 | 39.8 | 1 | Q4_K_M · llama.cpp · Linux · 8K ctx |
Self-host serving plan
Want to host Qwen 2.5 Coder 14Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
10.3 GB
8.9 GB weights + 0.9 GB KV
Aggregate tok/s
18
across 1 user
Per-user tok/s
18
14 B dense
✅ Fits in 24 GB VRAM with 13.7 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Qwen 2.5 Coder 14B?
Qwen 2.5 Coder 14B requires 8.87 GB VRAM minimum with Q4_K_M quantization. For full precision you need 15.12 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Qwen 2.5 Coder 14B?
To run Qwen 2.5 Coder 14B, you need a GPU with at least 8.9 GB of VRAM, but 15.1 GB is recommended for optimal performance.
Is Qwen 2.5 Coder 14B good for coding?
Yes, Qwen 2.5 Coder 14B is excellent for complex programming tasks due to its large context length of 32,768 tokens and 14 billion parameters.
Qwen 2.5 Coder 14B vs Llama 3.1 8B?
Qwen 2.5 Coder 14B has more parameters (14B vs 8B) and a longer context length (32,768 vs typically shorter), making it better suited for complex coding tasks.
Can I run Qwen 2.5 Coder 14B on a Mac?
Yes, you can run Qwen 2.5 Coder 14B on a Mac, provided your Mac has a compatible GPU with sufficient VRAM (8.9 GB minimum, 15.1 GB recommended).
How much VRAM does Qwen 2.5 Coder 14B need?
Qwen 2.5 Coder 14B requires 8.9 GB to 15.1 GB of VRAM, depending on the quantization level used.
Is Qwen 2.5 Coder 14B censored?
Qwen 2.5 Coder 14B is not inherently censored, but it adheres to community guidelines and ethical standards in its responses.
Is Qwen 2.5 Coder 14B commercial-use allowed?
Yes, Qwen 2.5 Coder 14B is licensed under Apache-2.0, which allows for commercial use.
Qwen 2.5 Coder 14B context length?
Qwen 2.5 Coder 14B has a context length of 32,768 tokens, allowing it to handle very long sequences of text.
Does Qwen 2.5 Coder 14B support function calling?
Qwen 2.5 Coder 14B supports function calling, enabling it to interact with external systems and APIs effectively.
Qwen 2.5 Coder 14B quantization options?
Qwen 2.5 Coder 14B supports various quantization options, including 8-bit and 4-bit, to reduce VRAM usage and improve performance.
Can Qwen 2.5 Coder 14B run on CPU?
While Qwen 2.5 Coder 14B can run on a CPU, it will be significantly slower compared to running on a GPU due to the model's size and complexity.
Qwen 2.5 Coder 14B fine-tuning?
Qwen 2.5 Coder 14B can be fine-tuned on custom datasets to improve its performance on specific tasks or domains.
Qwen 2.5 Coder 14B system requirements?
To run Qwen 2.5 Coder 14B, you need a system with a GPU that has 8.9 GB to 15.1 GB of VRAM, ample RAM (at least 32 GB recommended), and a powerful CPU.
Qwen 2.5 Coder 14B performance benchmark?
Qwen 2.5 Coder 14B processes around 50-100 tokens per second on a high-end GPU, depending on the quantization level and specific hardware configuration.
Qwen 2.5 Coder 14B for RAG?
Qwen 2.5 Coder 14B can be used for Retrieval-Augmented Generation (RAG) to enhance its context and generate more accurate and relevant responses.
Qwen 2.5 Coder 14B for agents?
Qwen 2.5 Coder 14B can be integrated into autonomous agents to provide advanced coding assistance and decision-making capabilities.
Qwen 2.5 Coder 14B for coding vs general?
Qwen 2.5 Coder 14B is optimized for coding tasks, with a larger context length and specialized training, making it more suitable for complex programming scenarios compared to general-purpose models.
Qwen 2.5 Coder 14B vs ChatGPT?
Qwen 2.5 Coder 14B is specifically designed for coding tasks, while ChatGPT is a more general-purpose language model. Qwen 2.5 Coder 14B excels in handling complex programming tasks and has a longer context length.
Qwen 2.5 Coder 14B download size?
The download size of Qwen 2.5 Coder 14B varies based on the quantization level, ranging from approximately 15 GB to 30 GB.
Best quant for Qwen 2.5 Coder 14B?
The best quantization for Qwen 2.5 Coder 14B depends on your hardware. 8-bit quantization offers a good balance between performance and VRAM usage, while 4-bit quantization is more memory-efficient but may have slightly reduced accuracy.