This piece realy made me think, and it totally complements your previous insights on LLM deployment, showing how crucial real-world performance is for shipping products.
We should mention CascadeFlow here (📌 https://github.com/lemony-ai/cascadeflow) — it’s a useful open-source model cascading tool that can cut costs and improve latency by automatically selecting cheaper models when appropriate.
The progression from llama.cpp for experimentation to vLLM for production traffic mirrors exactly how our team evolved over the past year. We burned so much time trying to optimize things manualy before discovering these tools. Your point about model quality rarly being the bottleneck is spot on and something more people need to hear. Saving this list for reference.
This piece realy made me think, and it totally complements your previous insights on LLM deployment, showing how crucial real-world performance is for shipping products.
We should mention CascadeFlow here (📌 https://github.com/lemony-ai/cascadeflow) — it’s a useful open-source model cascading tool that can cut costs and improve latency by automatically selecting cheaper models when appropriate.
The progression from llama.cpp for experimentation to vLLM for production traffic mirrors exactly how our team evolved over the past year. We burned so much time trying to optimize things manualy before discovering these tools. Your point about model quality rarly being the bottleneck is spot on and something more people need to hear. Saving this list for reference.
Great list, can't wait to check each one out! I have some experience with Ollama but haven't try anything else yet.