ProjectMetricLiterature anglevLLMtokens/s via benchmark_throughput.pyPagedAttention scheduling, prefix caching, speculative decodingSGLangtokens/s, TTFTRadixAttention, constrained decoding, chunked prefillllama.cpptokens/s via llama-benchOperator fusion, quantized matmul, cache-efficient attentionTensorRT-LLMtokens/s via benchmarks/Kernel fusion, KV cache optimization, in-flight batchingggmltest-backend-ops perfSIMD kernels, quantization formats, graph optimizationwhisper.cppreal-time factor via benchSpeculative decoding, batched beam searchWe also tried more established projects (Valkey/Redis, PostgreSQL, CPython, SQLite) and found it harder to surface improvements. Those codebases have been optimized by hundreds of contributors over decades, and the gains the agent found were within noise.
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