LLM Inference Cost Optimization: Run AI Inference for Less

LLM inference cost optimization on Kubernetes centers on improving low GPU utilization through workload sharing, continuous batching, quantization, right-sizing, and demand-driven autoscaling. Cast AI benchmark data cited in the material indicates that continuous batching can reduce Llama 3.1 70B serving costs on an H100 by three to four times for short-context workloads by avoiding idle time between requests, while its fleet report estimates average production GPU utilization at 5%. MIG can divide compatible GPUs into isolated partitions for independent smaller models, whereas time-slicing is less suitable for latency-sensitive production workloads and MIG cannot support tensor parallelism across partitions. vLLM features such as continuous batching, speculative decoding for low-concurrency latency reduction, and prefix caching for repeated chat or RAG prompts can further improve throughput and reduce prefill work. Quantization choices depend on GPU architecture and quality requirements, with FP8 favored on H100/H200 hardware and AWQ often presented as a practical way to run 70B models on an A100 80GB, though lower precision can reduce accuracy. The material also emphasizes matching GPU requests to observed memory and throughput needs, using inference-specific metrics such as queue depth and KV-cache pressure for scaling, and scaling idle endpoints to zero when cold-start latency is acceptable. Cast AI positions its platform as an integrated automation layer for these practices, citing a customer case study reporting a 70% reduction in GPU infrastructure costs.

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