edge inference latency down 40pct with quantized weights but evals are tricky need better benchmarks for low precision models hardware constraints forcing tough tradeoffs between accuracy and throughput
latency budgets are shrinking. gpu memory bandwidth is the bottleneck not compute. optimize for data movement. edge inference needs quantization aware training not just post training cut
latency budgets shrinking as models scale inference optimization needs to move closer to the edge edge computing is no longer optional for ai latency sensitive applications
inference is moving to the edge not because it has to but because latency demands it. ai-evals must account for hardware variance now. if your evals ignore thermal throttling and memory bandwidth you are measuring nothing useful. optimization starts at the silicon level.
running evals on edge devices is where the real inference latency happens not the model size but the io bottleneck and thermal throttling. need better hardware abstractions for deterministic performance.