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Qwen 2.5 Coder 14B: Specs, Published Scores and Local Setup
Qwen2.5-Coder-14B-Instruct is the mid-sized model in Alibaba’s Qwen2.5-Coder family, released on 12 November 2024 under Apache 2.0. On Ollama it is qwen2.5-coder:14b, an 8.99 GB download that fits a 12 GB GPU.
Specifications
From Qwen’s model card and config.json for Qwen2.5-Coder-14B-Instruct, the Qwen2.5-Coder technical report and the Ollama registry (read 2 November 2026).
📊 Published Benchmark Scores
These are Qwen’s own evaluations from the Qwen2.5-Coder Technical Report (arXiv:2409.12186v3, 12 November 2024), Tables 16 and 19. This site has not re-run them, and they date from the model’s release.
| Model | Size | HumanEval | HumanEval+ | MBPP | MBPP+ | BigCodeBench-Instruct (full) | Aider (pass@2) |
|---|---|---|---|---|---|---|---|
| Qwen2.5-Coder-14B-Instruct | 14B | 89.6 | 87.2 | 86.2 | 72.8 | 48.4 | 69.2 |
| Qwen2.5-Coder-7B-Instruct | 7B | 88.4 | 84.1 | 83.5 | 71.7 | 41.0 | 68.4 |
| Qwen2.5-Coder-32B-Instruct | 32B | 92.7 | 87.2 | 90.2 | 75.1 | 49.6 | 73.7 |
| DS-Coder-V2-Lite-Instruct | 2.4B active / 16B | 81.1 | 75.6 | 82.8 | 70.4 | 36.8 | 52.6 |
| Starcoder2-15B-Instruct-v0.1 | 15B | 67.7 | 60.4 | 78.0 | 65.1 | 37.2 | — |
| CodeLlama-13B-Instruct | 13B | 40.2 | 32.3 | 60.3 | 51.1 | 28.5 | 1.5 |
| GPT-4o (2024-08-06) | API | 92.1 | 86.0 | 86.8 | 72.5 | 50.1 | 74.4 |
Scores are pass rates in percent; “—” means the report does not list that model on Aider. More on the 7B, 32B, DeepSeek Coder V2 Lite and StarCoder2 15B pages.
Memory and Hardware
With 48 layers and 8 key-value heads of 128 dimensions, the f16 KV cache is 2 × 48 × 8 × 128 × 2 bytes = 192 KiB per token. The totals below add that cache to the 8.99 GB Q4_K_M file; they are arithmetic, not measurements, and the runtime needs a little more on top.
| Context | KV cache (f16) | Weights + cache |
|---|---|---|
| 4,096 tokens | 0.81 GB | ~9.8 GB |
| 8,192 tokens | 1.61 GB | ~10.6 GB |
| 32,768 tokens | 6.44 GB | ~15.4 GB |
| 131,072 tokens (full, with YaRN) | 25.8 GB | ~34.8 GB |
A 12 GB card (RTX 3060 12GB, RTX 4070) holds the Q4_K_M build with an 8K context; a 32K context wants a 16 GB card. Ollama picks a default context from your VRAM (4K below 24 GiB, according to its context-length documentation), and ollama ps shows whether any of the model has spilled to the CPU.
Speed ceilings, not measurements. A dense model reads its whole file once per generated token, so memory bandwidth divided by file size caps the rate: ~40 tok/s on an RTX 3060 12GB (360 GB/s), ~56 tok/s on an RTX 4070 (504 GB/s), ~112 tok/s on an RTX 4090 (1,008 GB/s) and ~17 tok/s on an Apple M3 Pro (150 GB/s). Real runs land below the ceiling. This site has not timed the 14B; the one Qwen coder it has measured is the 4.68 GB qwen2.5-coder:7b, at 28.0 tok/s on an Apple M3 Pro 18 GB (Ollama 0.12.3, 15 October 2026), against a ~32 tok/s ceiling on that machine.
Ollama builds
qwen2.5-coder:14b-instruct-q3_K_M: 7.34 GBqwen2.5-coder:14b (default, same file as 14b-instruct-q4_K_M): 8.99 GBqwen2.5-coder:14b-instruct-q5_K_M: 10.51 GBqwen2.5-coder:14b-instruct-q6_K: 12.12 GBqwen2.5-coder:14b-instruct-q8_0: 15.70 GB
File sizes from the Ollama registry, 2 November 2026.
📊 Cloud vs Local AI: Practical Differences
Cloud coding assistants such as GitHub Copilot need an internet connection, their paid plans are priced per user, and your code is processed on the provider’s servers. A local model like Qwen 2.5 Coder 14B runs on your own hardware: the code stays on the machine, it works offline and there is no per-seat fee, but you supply the GPU or RAM, and in Qwen’s own table above it scores a little below GPT-4o on every benchmark.
☁️ Cloud AI Considerations
- Subscription Model: Paid plans bill every month for continued access
- Internet Dependency: Requires consistent internet connectivity for operation
- Data Privacy: Code suggestions processed on external servers
- Cost Structure: Per-developer pricing scales with team size
🖥️ Local AI Advantages
- Data Security: Code and prompts stay on your own hardware
- Offline Operation: Functions without internet connectivity
- Cost: No per-seat licence fees; you pay for the hardware and the power
- Licence: Apache 2.0 weights, which allow commercial use
👨💼 Installation: Complete Professional Setup Guide
⚡ Quick Start
Qwen 2.5 Coder 14B is free to download under Apache 2.0 and runs entirely on your own machine through Ollama. The requirements below are for the default Q4_K_M build.
System Requirements
🚀 Installation Commands
These steps install Ollama, download the 8.99 GB default build and run a first prompt. To use the model inside your editor, see the Continue.dev + Ollama setup guide.
Install Ollama
Linux one-liner shown; the macOS and Windows installers are at ollama.com/download
Pull Qwen 2.5 Coder 14B
The default tag is the instruct model at Q4_K_M, an 8.99 GB download
Test Code Generation
Run a one-off prompt to check that the model loads and answers
Set the Context Length (optional)
Start the server with a fixed context (stop the background service first); 8,192 tokens fits a 12GB card, a 16GB card can take 32,768
What the terminal shows
❓ Frequently Asked Questions
How does Qwen 2.5 Coder 14B compare with GPT-4o on coding benchmarks?
In Qwen’s technical report (arXiv:2409.12186v3, Table 16) the 14B instruct model scores 89.6 on HumanEval and 86.2 on MBPP, against 92.1 and 86.8 for GPT-4o (2024-08-06). Those are Qwen’s own November 2024 evaluations. GitHub Copilot is a product that runs several cloud models, so there is no single score to set against it.
What hardware do I need?
The default qwen2.5-coder:14b build is 8.99 GB and needs about 9.8 GB with a 4K context, so a 12 GB GPU such as the RTX 3060 12GB holds it, and 16 GB of system RAM runs it on the CPU, more slowly. A 32K context adds 6.4 GB of KV cache, which calls for a 16 GB card.
Does my code leave my machine?
No. With Ollama the model runs on your own hardware, so prompts and code are not sent to a cloud service. The weights are released under Apache 2.0, which permits commercial use.
Which Ollama tag should I use?
qwen2.5-coder:14b is the instruct model at Q4_K_M (8.99 GB). The registry also has qwen2.5-coder:14b-instruct-q5_K_M (10.51 GB), qwen2.5-coder:14b-instruct-q6_K (12.12 GB) and qwen2.5-coder:14b-instruct-q8_0 (15.70 GB) if you have memory to spare.
Qwen 2.5 Coder 14B Local Deployment
Qwen 2.5 Coder 14B running on your own machine through Ollama: prompts and code stay local instead of going to a cloud API
📚 Resources & Further Reading
🔧 Official Qwen Resources
- Qwen2.5-Coder Series Announcement
Qwen blog, 12 November 2024: the release of the 14B and 32B
- Qwen Coder GitHub Repository
The former QwenLM/Qwen2.5-Coder repository, now QwenLM/Qwen3-Coder
- Qwen2.5-Coder-14B-Instruct Model Card
Weights, config and usage notes on Hugging Face
- Qwen Official Website
Comprehensive Qwen documentation
📖 Code Generation Research
- Qwen2.5-Coder Technical Report
arXiv:2409.12186, the source of the scores on this page
- StarCoder 2 and The Stack v2
BigCode’s paper behind StarCoder2, one of the comparison models
- HumanEval Benchmark
Code generation evaluation
- Latest Code Gen Research
Current research papers
💻 Programming Languages & Tools
- CodeBERT
Microsoft's code understanding model
- CodeT5
Salesforce code generation model
- Transformers Library
State-of-the-art ML framework
- Visual Studio Code
Leading code editor
📊 Code Benchmarks & Evaluation
- EvalPlus
HumanEval+ and MBPP+, the stricter test sets in the table above
- HumanEval Dataset
Benchmark programming problems
- Aider LLM Leaderboards
The code-editing benchmark behind the Aider column
- BigCode Evaluation
Large-scale code evaluation
🚀 Development & Deployment
- LangChain Framework
AI application development
- Semantic Kernel
AI orchestration framework
- vLLM Serving Framework
High-throughput model serving
- Llama.cpp Python
Efficient model inference
🏢 Alibaba AI Ecosystem
- Alibaba Cloud ML Platform
Enterprise AI services
- EasyNLP Framework
Alibaba's NLP toolkit
- DAMO Academy
Alibaba's research institution
🚀 Learning Path: Code Generation Expert
Code Generation Fundamentals
Understanding AI-assisted programming
Qwen Architecture
Mastering Qwen model capabilities
Development Integration
Building coding assistant applications
Advanced Applications
Production deployment and optimization
⚙️ Advanced Technical Resources
Code Generation Implementation
Research & Development
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