Self-hosted, memory-optimized LLM inference serving for constrained environments
Serve a self-hosted model to multiple concurrent users on existing GPU hardware by treating GPU memory, especially KV-cache capacity, as the primary scaling constraint. Reduce weight and cache memory usage with quantization, then validate the resulting user capacity and quality against real workloads.
From DevOps & AI Toolkit — Why Your GPU Fails at 3 Users (LLM Inference Isn't a Compute Problem) at 01:00
Problem: A model can fit in GPU memory and answer one request quickly while becoming unusably slow as concurrent conversations consume the shared KV cache.
For: Teams that must self-host models because of air-gapped environments, data-residency requirements, or models they fine-tuned themselves.
Products from this video
Examples
- An 8-billion-parameter model in 16-bit precision on one NVIDIA L4 used roughly 16 GB for weights, about 1 GB for the engine, and about 3 GB for KV cache; the engine estimated under three concurrent conversations at 8,000 tokens, while shorter conversations of a couple thousand tokens fit around a dozen.