vLLM

Cloud Ape’s vLLM expertise: maximizing throughput and minimizing latency for self-hosted LLM inference — continuous batching with PagedAttention KV cache management, tensor and pipeline parallelism across GPUs, AWQ/GPTQ quantization tradeoffs, Kubernetes deployment with autoscaling, and SLO-driven tuning for tokens-per-second per GPU dollar.