Qwen 2.5 14B by Alibaba is a large language model with 14 billion parameters, designed for advanced text generation tasks. This model excels in generating coherent and contextually relevant text across a wide range of applications, including content creation, chatbot interactions, and natural language understanding. With a context length of 131,072 tokens, Qwen 2.5 14B can handle extensive input sequences, making it suitable for tasks that require deep contextual understanding and long-form content generation. The model is licensed under the Apache-2.0 license, ensuring it is freely available for both research and commercial use.
In its size class, Qwen 2.5 14B holds its own, offering competitive performance and efficiency. While it is a hefty model requiring significant computational resources, it demonstrates strong capabilities in generating high-quality text, often outperforming smaller models in complex tasks. The available quantizations, Q4_K_M and Q8_0, help reduce the VRAM requirements to a more manageable range of 8.9 to 15.1 GB, making it feasible for users with mid-range to high-end GPUs. Ideal users include researchers, developers, and businesses looking to deploy robust text generation capabilities locally. Realistic hardware for running this model includes systems equipped with GPUs such as the NVIDIA RTX 3080 or higher, ensuring smooth and efficient operation.
| 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: 128K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Qwen 2.5 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 qwen2.5:14b - 2
Chat
ollama run qwen2.5:14b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"qwen2.5:14b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Qwen 2.5 14B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Qwen 2.5 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 14B?
Qwen 2.5 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 14B?
To run Qwen 2.5 14B, you need a GPU with at least 8.9 GB of VRAM, but 15.1 GB is recommended for optimal performance, especially for larger context lengths and higher precision.
Is Qwen 2.5 14B good for coding?
Yes, Qwen 2.5 14B is excellent for coding tasks, offering strong performance in generating code, understanding complex programming concepts, and providing detailed explanations.
Qwen 2.5 14B vs Llama 3.1 8B?
Qwen 2.5 14B has more parameters (14B vs 8B), which generally results in better performance in complex tasks like coding and reasoning, but requires more VRAM and computational resources.
Can I run Qwen 2.5 14B on a Mac?
Yes, you can run Qwen 2.5 14B on a Mac, but ensure your Mac has a compatible GPU with sufficient VRAM. M1/M2 chips with Metal support can also run the model efficiently.
How much VRAM does Qwen 2.5 14B need?
Qwen 2.5 14B requires between 8.9 GB and 15.1 GB of VRAM, depending on the quantization level used. Higher quantization levels reduce VRAM usage but may slightly impact performance.
Is Qwen 2.5 14B censored?
Qwen 2.5 14B is not inherently censored, but it adheres to ethical guidelines and content policies to ensure responsible use and avoid harmful or inappropriate content.
Is Qwen 2.5 14B commercial-use allowed?
Yes, Qwen 2.5 14B is licensed under the Apache-2.0 license, which allows commercial use as long as you comply with the terms of the license.
Qwen 2.5 14B context length?
Qwen 2.5 14B supports a context length of up to 131,072 tokens, making it suitable for handling very long documents and conversations.
Does Qwen 2.5 14B support function calling?
Yes, Qwen 2.5 14B supports function calling, allowing you to integrate external functions and APIs directly into the model's workflow.
Qwen 2.5 14B quantization options?
Qwen 2.5 14B offers several quantization options, including 8-bit and 4-bit, which reduce the model's size and VRAM usage while maintaining acceptable performance.
Can Qwen 2.5 14B run on CPU?
While Qwen 2.5 14B can run on a CPU, it will be significantly slower compared to running on a GPU. For best performance, use a GPU with sufficient VRAM.
Qwen 2.5 14B fine-tuning?
Yes, Qwen 2.5 14B can be fine-tuned on your own data to improve its performance on specific tasks or domains. Fine-tuning requires a powerful GPU and a significant amount of training data.
Qwen 2.5 14B system requirements?
Qwen 2.5 14B requires a system with at least 8.9 GB of VRAM, 64 GB of RAM, and a multi-core CPU. For optimal performance, a high-end GPU with 15.1 GB of VRAM and 128 GB of RAM is recommended.
Qwen 2.5 14B performance benchmark?
Qwen 2.5 14B processes approximately 100-200 tokens per second on a high-end GPU, with performance varying based on the specific hardware and quantization level used.
Qwen 2.5 14B for RAG?
Yes, Qwen 2.5 14B is well-suited for Retrieval-Augmented Generation (RAG) tasks, where it can effectively combine information from external sources with its own knowledge to generate high-quality responses.
Qwen 2.5 14B for agents?
Qwen 2.5 14B can be used to create intelligent agents for various applications, such as chatbots, virtual assistants, and automated customer service, thanks to its strong reasoning and natural language processing capabilities.
Qwen 2.5 14B for coding vs general?
Qwen 2.5 14B excels in both coding and general tasks, but it is particularly strong in coding due to its extensive training on programming-related data and its ability to generate high-quality code.
Qwen 2.5 14B vs ChatGPT?
Qwen 2.5 14B has more parameters (14B vs 175B for the largest ChatGPT model) and is optimized for local deployment, making it more resource-efficient. However, ChatGPT may offer better performance in some general tasks due to its larger size and diverse training data.
Qwen 2.5 14B download size?
The download size of Qwen 2.5 14B varies depending on the quantization level. The full model is approximately 28 GB, while 8-bit and 4-bit quantized versions are around 14 GB and 7 GB, respectively.
Best quant for Qwen 2.5 14B?
The best quantization for Qwen 2.5 14B depends on your hardware and performance needs. 8-bit quantization is a good balance between VRAM efficiency and performance, while 4-bit is ideal for systems with limited VRAM.