Deploying Custom Docker Model Inference APIs for Production

The Architecture of a Modern Inference API

Building a production-grade inference API starts with selecting the right serving engine. The industry has standardized on containerized environments that package the model weights, the inference server, and the necessary CUDA dependencies into a single, reproducible unit. This approach eliminates the 'it works on my machine' problem that frequently plagues GPU-accelerated workloads.

The core of your stack will likely be an open-source inference engine. vLLM remains the preferred choice for high-throughput batching, while TensorRT-LLM is optimized for peak hardware efficiency on NVIDIA GPUs. These engines now integrate with NVIDIA Dynamo, an inference operating system that coordinates GPU and memory resources across clusters. Dynamo introduces smarter traffic control and GPU-to-GPU data routing, which NVIDIA reports can boost performance on Blackwell-class hardware compared to naive implementations.

• Container Runtime

  Use the NVIDIA Container Toolkit to expose host GPUs to your Docker environment.

• Serving Layer

  Engines like vLLM provide a built-in OpenAI-compatible server, making them drop-in replacements for existing SDKs.

• Orchestration

  Tools like Dynamo manage the KV cache and memory movement, reducing the frequency of Out-Of-Memory (OOM) errors during high concurrency.

Performance Benchmarks and Engine Selection

Choosing between vLLM, TensorRT-LLM, and newer entrants like SGLang depends on your specific workload shape. vLLM's PagedAttention algorithm was built for scenarios with variable request sizes and spiky traffic. The paper that introduced it reports near-zero waste in KV cache memory and 2 to 4 times the throughput of the serving systems that preceded it at the same latency [4]. Its ability to manage memory without fragmentation makes it a stable choice for multi-tenant APIs.

For fixed-shape, high-QPS services, TensorRT-LLM often wins on tail latency. CUDA graph fusion removes per-step launch overhead and deep quantization paths (FP8/INT4) cut the bytes moved on every decode step, so the gap between output tokens tightens. Time-To-First-Token is a different measurement: it includes prefill, so it scales with prompt length and stays orders of magnitude above a single decode step. However, this comes at the cost of operational complexity, as it requires pre-compiling model engines for specific GPU architectures.

If your application involves multi-turn conversations or Retrieval-Augmented Generation (RAG), SGLang is worth considering. Its RadixAttention mechanism reuses the KV cache across requests that share a prefix, and the SGLang paper reports up to 6.4x higher throughput than the systems it was measured against on workloads including multi-turn chat and RAG [5].

The Sovereignty Moat: GDPR and the CLOUD Act

For European AI teams, the technical choice of an inference engine is often secondary to the legal requirement of data residency. A common mistake is assuming that selecting an 'EU region' on a US-based hyperscaler satisfies GDPR requirements. Under the US CLOUD Act, American authorities can compel US-based companies to hand over data regardless of its physical storage location. This creates a significant compliance risk for startups handling sensitive medical, financial, or manufacturing data.

EU law defines no 'sovereignty' status. The highest Gaia-X label level requires infrastructure that is both operated exclusively in the EEA and provided by an entity whose headquarters and main establishments are in the EEA. Lyceum holds no Gaia-X label and does not claim one. What Lyceum does offer is a European inference platform where customer data is processed in European data centers in Spain, Paris and the Nordics, through European legal entities headquartered in Berlin and Zürich. Documented processing locations and contracting entities are what enterprise clients asking for provable data residency actually review.

1. Data Residency

   Ensure the GPU provider has no US-based parent company subject to the CLOUD Act.

2. GDPR Compliance

   Verify that the provider offers a Data Processing Agreement (DPA) that explicitly covers GPU workloads.

3. Sovereign Infrastructure

   Ask where the capacity physically sits and which legal entity operates it, rather than accepting an EU region label on a US-owned platform.

Cost Optimization and Hardware Selection

Inference costs are driven by two factors: the hourly rate of the GPU and the efficiency of the serving stack. The NVIDIA H100 remains the workhorse for 70B parameter models. For larger models exceeding 100B parameters, the B200 (Blackwell) is necessary, though its higher cost requires high utilization to be economical.

Startups can achieve significant cost savings by moving off hyperscalers to specialized providers that offer H100 VMs at competitive rates and eliminate egress fees. Egress charges are a hidden tax on AI companies, especially those performing batch OCR or medical image processing where large datasets are moved in and out of the cloud.

Another critical cost-saving feature is scale-to-zero. By shutting down inference nodes during idle periods, teams only pay for the compute they actually use. While this introduces a slight cold-start latency for the first request, the financial benefits for non-24/7 workloads are substantial. Per-second billing ensures that these savings are captured accurately, without the 'started hour' penalties common among older providers.

Deployment Workflow: From Docker to Endpoint

The final step is exposing your containerized model as a secure API. A production-ready Dockerfile should pin specific versions of the CUDA toolkit and the inference engine to prevent breaking changes during redeployment. For a vLLM-based deployment, your Docker Compose stack should include health checks and a reverse proxy for load balancing.

# Example vLLM Production Dockerfile
 FROM vllm/vllm-openai:v0.6.0
 ENV NVIDIA_VISIBLE_DEVICES=all
 COPY./custom_kernels /app/kernels
 ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
 CMD ["--model", "meta-llama/Llama-3.3-70B-Instruct", "--gpu-memory-utilization", "0.95"]

Once the image is built, it can be deployed to a dedicated inference endpoint. This setup provides a unique endpoint URL, shown in your Lyceum dashboard, which is 100% compatible with the OpenAI SDK. Engineers can switch from a managed API to their custom Docker endpoint by changing a single line of code in their application: the base_url. This portability ensures that you are never locked into a single provider and can move your workloads as your scaling or compliance needs evolve.

Sources

[1] NVIDIA: Container Toolkit Documentation; [2] vLLM: Using Docker; [3] NVIDIA: Triton Inference Server User Guide; [4] arXiv: Efficient Memory Management for Large Language Model Serving with PagedAttention, read 3 August 2026; [5] arXiv: SGLang: Efficient Execution of Structured Language Model Programs, read 3 August 2026