Prefill-decode disaggregation separates the two phases of LLM inference — prompt processing and token generation — onto dedicated GPU pools, eliminating the head-of-line blocking that causes latency spikes under concurrent load. Production deployments report 1.5x to 2.5x throughput gains, with cache-aware variants like Together AI’s CPD pushing improvements to 40%. …
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vLLM vs SGLang: Which Engine Actually Wins in 2026?
On H100 SXM5 80GB running Llama 3.3 70B Instruct at FP8, SGLang serves 1,920 tokens per second at 50-way concurrency — just 3.8% faster than vLLM’s 1,850. But swap to Llama 3.1 8B, and that gap explodes to 29%: SGLang hits 16,200 tok/s versus vLLM’s 12,500. The inference engine you …
Serverless GPU Cold Starts Take 40s – Here’s How to Fix
The 1000x Latency Gap A cold-start instance on a serverless GPU platform produces its first token after more than 40 seconds. A warm instance generates subsequent tokens in roughly 30 milliseconds. That is a latency ratio of over 1,300:1 between the cold and warm states, and it is the single …
Google TPU v8 Puts KV Cache on Silicon to Cut Inference Cost
Google Put KV Cache on Silicon Google’s TPU 8i triples on-chip SRAM to 384 MB and crams 288 GB of HBM onto a single chip — enough to host massive KV caches entirely in silicon, bypassing the memory wall that has bottlenecked LLM inference since the transformer era began. The …