Getting the most from your LLM inference system

LLM inference generates responses from trained models through a parallel, compute-oriented prefill phase and a sequential, often memory-bandwidth-limited decode phase, making performance dependent on workload characteristics rather than a single tokens-per-second figure. Slow generation may result from CPU fallback, GPU memory spillover, inadequate serving software, or insufficient VRAM for both model weights and the growing key-value cache, which expands with context length and concurrent requests. The discussion recommends prioritizing GPU memory capacity and bandwidth over raw compute for many single-stream workloads, while using quantization selectively because lower precision can reduce memory use but may affect quality or add overhead. It contrasts local tools with production engines that improve concurrent serving through continuous batching, prefix caching, speculative decoding, and other scheduling techniques, and notes that self-hosting costs must account for utilization, operations, scaling, compliance, and latency needs. A central argument is that the KV cache is evolving from local GPU memory into shared system state, driving interest in cache offloading, paged attention, prefill-decode disaggregation, and distributed storage designed for predictable low-latency access. Effective operations therefore require measuring time to first token, inter-token latency, throughput, tail latency, queue depth, and memory pressure rather than relying solely on average latency or GPU utilization.

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