With over 40 million downloads and more than 50,000 derivative models created, Qwen 2.5 is increasingly becoming a preferred choice of developers for AI agent development. Its combination of performance, efficiency, and versatility makes it a popular choice for developers building the next generation of AI agents.
Things took an unexpected turn when a developer on Reddit, who built an enterprise-grade agent framework, reported that Qwen 2.5 14B outperformed both GPT-4 and GPT-4o for specific applications due to its capabilities in function calling, chain-of-thought reasoning, and following complex instructions.
Contrary to the popular misconception that AI applications developed in China are not secure enough, Qwen stands out as a trusted option for enterprises. With Qwen 2.5, enterprise users can successfully implement Qwen 2.5 by following strict isolation protocols such as vLLM in completely air-gapped environments, ensuring no external communications are possible.
After the launch of the Qwen 2.5 coder, the narrative shifted, and developers started calling it the best LLM for coding. One developer on Reddit shared his benchmarks under a power-constrained environment with a midrange GPU 3090, and the results were shocking. Even with 3090, he achieved 28 tok/sec at 32K context, which is readily usable for many coding situations.
Optimised for Consumer-grade GPUs: A Huge Win for Devs
GPUs are one of the core aspects of AI development, and the easiest way to boost AI development is to build solutions for GPUs that are available to a large number of developers. Enterprise-grade GPUs are quite expensive. Even the latest generation of consumer GPUs like NVIDIA 4090 are beyond what every developer can afford, and that is where the Qwen 2.5 shines.
The breakthrough in running Qwen 2.5 on limited hardware comes through a layer-by-layer inference technique developed through the AirLLM project. With this technique, rather than loading the entire model at once—which would require extensive GPU memory—the system processes the model one layer at a time.
Loading the entire model into video random access memory (VRAM) simultaneously reduces the maximum VRAM requirements, making it possible to run even the 72B parameter model on systems with as little as 4GB of VRAM.
Multiple users have praised Qwen for its efficiency. One developer, in particular, was developing an agent for automatic grammar correction of texts in Italian. After testing all the models available that could run on the hardware he had for the project (8GB VRAM—32GB RAM), he confirmed that the best results were achieved with Qwen 2.5-14B.
Qwen 14B instruct is also known to perform well with SQL tools as it is specifically tuned for instruction-following tasks, which include understanding and accurately generating structured SQL queries. Another developer mentioned that in his testing, under the 27b size, Qwen 14B instruct is the only model that can use the SQL tools and give good answers.
Is it the Best LLM to Run Locally for Coding?
Developers’ practical experiences have been notably positive. Many report successfully integrating Qwen 2.5 into their development environments using tools like Llama.cpp, LM Studio API, and VSCodium, among others. The model’s strong instruction-following capabilities and ability to generate precise JavaScript Object Notation (JSON) outputs have made it particularly valuable for enterprise development workflows.
Beyond its ability to run on mid-range hardware, developers have been finding it better than popular LLMs like ChatGPT. One developer shared that since he started using Qwen 2.5 35B for coding tasks, he has not touched ChatGPT and only uses Claude for planning.
“It is local, and it helps with debugging and generates good code. I do not have to deal with the limits on ChatGPT or Sonnet. I am also impressed with its ability to follow instructions and JSON output generation,” he added further.
A developer who extensively tested the model said he created a fully functional Pac-Man game in Python using the 72B model running locally in Q4 quantisation, complete with ghosts, playable map, and sprite loading functionality, outperforming Claude which only managed a basic map implementation.
For developers seeking to reduce dependency on cloud-based solutions, Qwen might be a great choice considering the $0.38 per million tokens they get compared to GPT-4o’s $5.0 and Claude 3.5 Sonnet’s $3.05.
While the model excels in many areas, it occasionally responds in Chinese when confused. Notably, this is not a major concern and can be solved with well-crafted prompts. In addition to this, some developers observe that while the model excels at code generation, it may require more precise prompting for handling complex tasks, particularly when compared to cloud-based alternatives.
The post Qwen 2.5 is Winning the AI Agents Race appeared first on Analytics India Magazine.
