We need to cut our LLM token latency in half by the end of the day, what streaming or batching configurations should I change in our backend code?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
We need to cut our LLM token latency in half by the end of the day, what streaming or batching configurations should I change in our backend code?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
LLM inference serving latency optimization continuous batching streaming TTFT inter-token latency official documentation Google ↗
vLLM serving performance optimization chunked prefill prefix caching speculative decoding documentation Google ↗
- Optimization and Tuning · docs.vllm.ai
- serve · docs.vllm.ai
- serve · docs.vllm.ai
- Metrics · docs.nvidia.com
- Text Generation Inference · huggingface.co
- Disaggregated Prefilling (experimental) · docs.vllm.cc
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
LLM inference serving latency optimization continuous batching streaming TTFT inter-token latency official documentation OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
vLLM serving performance optimization chunked prefill prefix caching speculative decoding documentation OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 |