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Performance Optimization

11 summarised stories about Performance Optimization, each linking back to the original source. Browse all topics →

Friday, 29 May 2026

How Together AI built the world’s fastest speech-to-text stack

Together AI 1 month ago

Together AI optimized its speech-to-text serving stack to transcribe 20 hours of audio in under 10 seconds using NVIDIA's Parakeet-TDT 0.6B v3 model. Key improvements included multi-profile TensorRT compilation for variable audio lengths, moving decoder branch logic onto GPU with conditional CUDA graphs for 2-3x faster decoding, eliminating redundant CPU copies through shared memory and Unix domain sockets, using epoll for streaming connection handling, and calling gc.freeze() to prevent garbage collection pauses. The changes address the full data path from preprocessing through GPU inference, reducing latency and tail latency spikes in production voice agent systems.

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Hugging Face Blog 1 month ago

PyTorch's torch.profiler module provides tools to analyze performance bottlenecks by exporting profiler tables showing execution time statistics and profiler traces showing temporal operation sequences across CPU and GPU. Running a 64×64 matrix multiplication and addition operation showed the GPU idle 99% of the time due to launch overhead, but increasing to 4096×4096 matrices shifted the workload from overhead-bound to compute-bound, with GPU kernel execution time rising from 23 microseconds to 4.495 milliseconds. Understanding these profiling artifacts—tables for identifying hotspots and traces for investigating kernel dispatch chains and CPU-GPU synchronization gaps—enables developers to systematically identify and address performance constraints in PyTorch models.