FP8 Mixed-Precision Serving in Production: Comparing E4M3 vs. E5M2 Formats, Per-Tensor vs. Block-Wise Dynamic Scaling, FP8 KV Cache, and Tensor Core GEMM Economics
FP8 Mixed-Precision Serving in Production: Comparing E4M3 vs. E5M2 Formats, Per-Tensor vs. Block-Wise Dynamic Scaling, FP8 KV Cache, and Tensor Core GEMM Economics Serving large language models at enterprise scale requires balancing computational throughput against high-bandwidth memory (HBM) capacity. While 16-bit floating-point formats (FP16 and BF16) remain the standard for model pre-training and fine-tuning, their memory footprint and arithmetic bandwidth create severe bottlenecks during hi
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