GPU Model Serving is for teams that need more control over throughput, cost, and model behavior. CODEPOP plans the serving engine, autoscaling direction, cold-start strategy, observability, health checks, and deployment rhythm around the model workload.
AI & LLM Model Hosting
GPU Model Serving
Production GPU hosting for open-source models, batch jobs, streaming responses, and scale-sensitive AI features.
Autoscaling
GPU
Observability
vLLM
AI Model Hosting
GPU
GPU Model Serving
Inference layer
vLLM, TGI, Ollama, Llama.cpp, containerized model servers, and queue-backed workers.
Security and governance
Image scanning direction, isolated runtimes, private endpoints, and scoped operational access.
LLMOps note
Designed around visibility into latency, token throughput, errors, and spend.