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常见问题解答

未来发展趋势如何？

从多个维度综合研判，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.

普通人应该关注哪些方面？

对于普通读者而言，建议重点关注¹ 垃圾邮件、"乞求赏金"提交，以及日益增多的零成本LLM生成内容。↩

关于作者

刘洋，资深编辑，曾在多家知名媒体任职，擅长将复杂话题通俗化表达。