TLDR Tech / Data
What is inference engineering? Deepdive
LLM inference has become a core production concern as open models mature, making inference engineering relevant beyond frontier labs. The stack spans runtime, infrastructure, and tooling, with common optimizations like batching, caching, quantization, speculative decoding, tensor/expert parallelism, and disaggregated prefill/decode. At scale, these techniques can cut latency, improve uptime to 99.99%+ in dedicated deployments, and reduce cost by 80%+ versus closed-model APIs.
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