vLLM Co-Designs DeepSeek Inference Stack

The hot take is that inference optimization is becoming model- and hardware-specific systems engineering, with broad gains coming from coordinated changes across the stack.

• –Precision selection balances memory bandwidth, tensor-core utilization, and output quality.
• –Fused DeepSeek attention kernels reduce launch overhead and global-memory round trips.
• –Programmatic dependent launch helps overlap dependent GPU work and cut synchronization costs.
• –Speculative decoding attacks autoregressive latency when draft acceptance outweighs verification overhead.
• –Developers should benchmark the complete serving path rather than optimize KV-cache settings in isolation.