Reasoning workloads shift LLM inference to a capacity-bound regime where KV-cache fragmentation limits data parallelism, tensor parallelism unlocks memory at the 32B scale, and MoE models require hybrid strategies to avoid routing latency.
Accelerating LLM inference throughput via asynchronous KV cache prefetching,
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Understanding Inference Scaling for LLMs: Bottlenecks, Trade-offs, and Performance Principles
Reasoning workloads shift LLM inference to a capacity-bound regime where KV-cache fragmentation limits data parallelism, tensor parallelism unlocks memory at the 32B scale, and MoE models require hybrid strategies to avoid routing latency.