2026.07.14 20:45

Cohere just open-sourced hardware-aware speculative decoding in vLLM and explained the full architecture - better than $3000 inference engineering courses. request arrives -> draft model generates candidate tokens -> main model verifies in parallel -> accepted tokens skip full forward pass -> 2.3x throughput on identical hardware. That loop is why production stacks running vLLM 0.6 are now serving at half the cost of last quarter. vLLM + speculative decoding + FP8 quantization + hardware-aware scheduling - that's the stack. Watch and save it, then upgrade your inference pipeline.

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