Distilly vs vLLM
Side-by-side comparison of two AI agent tools
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Distillyopen-source
Distilly — Distill how they think into reusable Skills for any Agent or Bot. Formerly Colleague Skill（原同事 Skill）.
vLLMopen-source
A high-throughput and memory-efficient inference and serving engine for LLMs
Metrics
| Distilly | vLLM | |
|---|---|---|
| Stars | 25.2k | 93.0k |
| Star velocity /mo | 2.1k | 3.0k |
| Commits (90d) | 31 | 3.9k |
| Releases (6m) | 0 | 10 |
| Overall score | 0.5824156271153126 | 0.9136864863110344 |
Pros
- +Exceptional serving throughput with PagedAttention memory optimization and continuous batching for production-scale LLM deployment
- +Comprehensive hardware support across NVIDIA, AMD, Intel platforms and specialized accelerators with flexible parallelism options
- +Seamless Hugging Face integration with OpenAI-compatible API server for easy model deployment and switching
Cons
- -Requires significant GPU memory for optimal performance, limiting accessibility for resource-constrained environments
- -Complex setup and configuration for distributed inference across multiple GPUs or nodes
- -Primary focus on inference means limited support for training or fine-tuning workflows
Use Cases
- •Production API serving for applications requiring high-throughput LLM inference with multiple concurrent users
- •Research and experimentation with open-source LLMs requiring efficient model switching and testing
- •Enterprise deployment of private LLM services with OpenAI-compatible interfaces for existing applications
FAQ
- Which is more popular, Distilly or vLLM?
- vLLM has more GitHub stars (92,998 vs 25,175).
- Which is more actively developed, Distilly or vLLM?
- vLLM had more commits in the last 90 days (3,922 vs 31).
- Should I use Distilly or vLLM?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.