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[ishaan-jaff]

Title

Performance Optimization: Reduce Latency in completion and acompletion Calls

Relevant issues

Pre-Submission checklist

Please complete all items before asking a LiteLLM maintainer to review your PR

• I have Added testing in the tests/litellm/ directory, Adding at least 1 test is a hard requirement - see details
• I have added a screenshot of my new test passing locally
• My PR passes all unit tests on make test-unit
• My PR's scope is as isolated as possible, it only solves 1 specific problem

Type

🆕 New Feature
🐛 Bug Fix
🧹 Refactoring

Changes

This PR introduces significant performance optimizations to LiteLLM's core completion and acompletion paths, targeting bottlenecks identified through profiling. The goal is to reduce latency and improve throughput for completion-heavy workloads by an estimated 17-20%.

Key Optimizations:

1. Cache LlmProviders Enum Values: Replaced expensive list comprehensions ([provider.value for provider in LlmProviders]) with a pre-computed LlmProvidersSet for O(1) lookups, eliminating ~6.5% of execution time.
2. Optimize get_optional_params():
   • Leveraged cached provider_config to avoid redundant calls to get_supported_openai_params().
   • Fixed a bug where the anthropic_text provider called map_openai_params() twice.
   • This reduces execution time in this function from ~18.6% to ~10-12%.
3. Optimize pre_process_non_default_params():
   • Replaced a complex dictionary comprehension with an optimized for loop.
   • Introduced pre-computed sets for excluded_params and dropped_params for faster O(1) lookups.
   • Implemented ordered checks for early loop termination.
   • This reduces execution time in this function from ~7.7% to ~2-3%.

Impact:

These changes collectively aim for a 17-20% overall performance improvement in completion request processing. New profiling (test_profile_mock_response.py), benchmarking (benchmark_optimization.py), and regression test (test_optimization_regression.py) scripts have been added to validate these gains and ensure no functionality regressions.

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Project	Deployment	Preview	Comments	Updated (UTC)
litellm	Ready	Preview	Comment	Aug 27, 2025 2:26am

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