Model Serving

Designing and deploying local and private cloud model serving infrastructure with vLLM, unified LiteLLM API gateways, spending controls, and Keycloak authentication.

Est. Duration2 - 4 Weeks
FormatEngineering Sprints
Delivery ModeRemote / On-site

Technical Context

Enterprise deployment of large language models requires balancing inference throughput, hardware costs, and data locality. While commercial cloud APIs offer rapid setup, they present challenges regarding data privacy, compliance, unpredictable API latencies, and vendor lock-in.

Local and private cloud model serving runs open-weight models on dedicated infrastructure. Optimizing the serving software stack ensures that hardware resources (GPU memory and compute units) are utilized efficiently.

Inference Runtimes

Using modern inference engines such as vLLM allows servers to leverage PagedAttention, continuous batching, and tensor parallelism. These techniques reduce memory fragmentation in KV-caches and maximize token generation throughput across concurrent user requests.

Gateway Routing

A centralized proxy gateway (LiteLLM Proxy) standardizes client communication through an OpenAI-compatible API interface. The gateway manages model routing, fallback endpoints, rate limits, and per-user token quotas, secured via Keycloak OpenID Connect authentication.

Module Scope

  • High-throughput local model serving using vLLM with PagedAttention and continuous batching
  • Deployment of open-weight models (Hermes, Llama, Qwen, DeepSeek) on dedicated enterprise hardware
  • API gateway routing, fallback handling, and token budget management via LiteLLM Proxy
  • Access control, user authentication, and rate limiting integrated via Keycloak OIDC/RBAC

Required Inputs

  • Infrastructure specifications (on-premise servers, private cloud instances, or dedicated hardware)
  • Hardware inventory (GPU accelerators, host memory, PCIe topology, storage speed)
  • Identity provider configuration (OIDC, SAML, LDAP, or Active Directory)
  • Target open-weight model architectures and parameter sizes
  • Concurrency requirements, expected query volumes, and latency targets

Technical Deliverables

  • Hardware sizing calculation and compute architecture specification
  • Containerized deployment configurations (Docker Compose, Kubernetes manifests, Helm charts)
  • Optimized vLLM inference configuration files
  • LiteLLM Proxy configuration with authentication and routing rules
  • Benchmark evaluation report covering TTFT, throughput, and memory utilization
  • Operational runbook for maintenance, updates, and monitoring

Delivery Methodology

The model serving engineering workflow establishes a dedicated compute layer:

  1. Compute Sizing: Assessing workload requirements, memory bandwidth constraints, and sizing GPU and host hardware.
  2. Inference Deployment: Configuring vLLM runtime parameters, tensor parallelism, quantization, and batching thresholds.
  3. Gateway Configuration: Deploying LiteLLM Proxy with authentication backends, model routing tables, and rate limiters.
  4. Throughput Benchmarking: Benchmarking latency, time-to-first-token (TTFT), and throughput under simulated concurrent loads.