Fix concurrency/scaling when many Python threads do streaming using *sync* completions#14816
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2 commits merged intoBerriAI:mainfrom Sep 24, 2025
Merged
Fix concurrency/scaling when many Python threads do streaming using *sync* completions#148162 commits merged intoBerriAI:mainfrom
2 commits merged intoBerriAI:mainfrom
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Problem
I have 14 Python threads doing streaming LLM requests concurrently.
Expectation: this should be mostly network-IO-bound workload, so this should scale reasonably well.
Reality: the performance is practically serial, i.e., this executes just a little faster than L * N / nCPU, where L is the latency of one such request (streaming or not), N is the number of requests, and nCPU is the number of (v)CPUs available for the Python process.
Pre-Submission checklist
Please complete all items before asking a LiteLLM maintainer to review your PR
tests/litellm/directory, Adding at least 1 test is a hard requirement - see detailsmake test-unitType
🆕 New Feature
🐛 Bug Fix
Changes
Guard
executor.submit()withif not litellm.disable_streaming_loggingin the hot path instreaming_handler.py's__next__(). This is a no-brainer change, sincerun_success_logging_and_cache_storage()is exactly a no-op iflitellm.disable_streaming_loggingis True, so submitting a no-op to an executor doesn't make any sense.Update dependency to
httpcoreto Don't hold lock unless necessary in PoolByteStream.close() encode/httpcore#1038.Make sync transport configurable via
litellm.sync_transport. I could have avoided this by changingclientaltogether, but this is a quality of life change.Then, in my code when I use litellm, I pre-configure the HTTPTransport in the following way: