Great write up!
Does batching add data from multiple requests into the same context, potentially decreasing perplexity? If so, are we trading off perplexity for lower operating costs?
Great write up. We use vLLM kv cache and continuous batching as a foundation for requests in ScalarLM and also add batching optimizations in a centralized queue and by adding explicit batching support in our client.
There is more perf you can sqeeuze out of vLLM
Hi, I'm the author of this post. Writing it was a great learning experience. I gained a lot of insight into vLLM. If you have any feedback or questions, feel free to drop a comment below!
Thanks for this! Learnt a lot.
Curious to understand how do we ensure that the same model instance gets requests from the same client/user? Since conversations are stateful and the model needs context from previous turns of the conversation.
Is this happening at the load balancer layer?
It's either sticky sessions or an lb that keeps track of prior sequences and route to the instance with the largest match. https://docs.sglang.ai/router/router.html
They’re not stateful, you submit the entire history with every call. Caching of prompts etc makes it important for performance to have sticky sessions or smth at the load balancer layer
Great write up, it would be interesting to see a lot of those covered features in comparison to other frameworks!
Thanks for writing the article!
I didn't quite get
Note that during the prefill phase, all prompt tokens from a request can be processed in one batch. This is possible because the query (Q) tensors, calculated from the tokens immediately before them, are available for each prompt token position.
I know that in practice prefill is much faster than inference. Would watching the 2h video from Karpathy help me understand why?
That snippet is trying to say that you can calculate KV for all the input tokens at once, and you don't need to loop over them since you have them all available.
Instead for decode, you need to sequentially generate each token.
And on the topic of prefill: Do you know what the role of GPUs is vs. in inference?
Prefill is part of Inference. It's the first major step where you calculate all the keys and values for the input tokens.
Decode is the next major step where you start generating output tokens one at a time.
Both run on GPUs but have slightly different workloads
1. Prefill has very little I/o from VRAM to HBM and more compute 2. Decode is light on compute but have to I/o the keys and values computed in the prefill stage for every output token
Doesn't decode also need to stream in the whole of the model weights, thus very I/O heavy?
Thanks for writing this up! I learnt a bunch from it. I noticed this didn’t discuss additional layers of caching - I can see how it would fit in, but is prompt caching out of the scope of this system?
Thanks, good read!
Batching in vLLM doesn't combine prompts into the same context - it processes separate requests in parallel while sharing compute resources, so there's no perplexity tradeoff, just efficiency gains.
It's worth noting that reason this works is because basically every LLM architecture currently in use is severely limited by memory bandwidth, not by compute. So it's trivial to run several requests at a time, while waiting for the next weights to arrive from VRAM.
I would like to know what inference speeds they are achieving exactly on what hardware. I skimmed and searched the article and didn't find that info.