your models Different models have different strengths and tradeoffs related to task complexity, latency, and cost. As we’ll see in the next section on Orchestration, you might want to consider using a 02 Focus on meeting your accuracy target with the best models available 03 Optimize for cost and latency by replacing larger models with smaller ones 
 where possible You can find a comprehensive guide

the competitive pressure amongst LLM providers increases – not on accuracy alone, but also on latency, uptime, and cost-per-token*. What used to cost dollars can now cost pennies. And what cost pennies builds high-speed interconnects that move data between GPUs and memory systems with minimal latency – an increasingly important performance constraint. These firms aren’t building foundation models the competitive pressure amongst LLM providers increases – not on accuracy alone, but also on latency, uptime, and cost-per-token*. What used to cost dollars can now cost pennies. And what cost pennies

https://github.com/Xilinx/ml-suite/blob/master/examples/caffe/Benchmark_README.md Two measurements we track: Latency & Throughput ˃ ML pipeline contains multiple stages, performance limited by slowest one ˃ Performance

sizes 。 Vectorized load/store for higher bandwidth utilization 。Double buffer to hide memory load latency 。 storage align to reduce bank conflicts of shared memory 。 Virtual threads for data reuse (on

Amazon Web Services, Inc. or its Affiliates. All rights reserved. Performance Comparison • Metric – Latency in ms for batch size = 1 • 1.7x speedup on Inception asymmetric quantized model • Mobilenet requires