AI Practice›AI Infrastructure

The GPU stack your AI needs to run in production.

Most AI projects stall at the infrastructure layer. We design and operate the full MLOps stack — GPU clusters, inference servers, model registries, feature stores, and CI/CD pipelines — so your models actually serve traffic.

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inference cluster · us-east-1

4× A100 80GB active

GPU util 73% · 8 concurrent requests

vLLM serving: 2,340 tok/s

p99 latency 1.2s · throughput +3.1×

Model registry: v2.1.4

3 stages: dev → staging → prod

Spot savings: 61%

vs on-demand · auto-fallback enabled

What we deliver

Production-grade MLOps, from compute to monitoring.

GPU provisioning

A100/H100 cluster design, spot instance strategy, autoscaling policies, and cost controls for training and inference workloads.

AWS · GCP · Lambda Labs · RunPod

Inference serving

High-throughput LLM serving with continuous batching, speculative decoding, and KV cache management for maximum tokens/second.

vLLM · TGI · Triton · TensorRT-LLM

MLOps pipelines

Automated training → evaluation → staging → production promotion pipelines with rollback, canary release, and shadow mode.

Kubeflow · Ray · Metaflow · Argo

Feature stores

Online and offline feature serving with point-in-time correctness, lineage tracking, and low-latency retrieval for real-time inference.

Feast · Tecton · Redis · DynamoDB

Model registry

Versioned artifact storage, experiment lineage, model cards, and staged promotion with approval gates before production.

MLflow · W&B · HuggingFace Hub

Model monitoring

Data drift, concept drift, output quality, and latency SLA monitoring — with auto-alerts and retraining triggers when needed.

Evidently · Grafana · Datadog

Our approach

Infrastructure first, models second.

Compute design

Right-size training and inference fleets before you spend. Spot strategy, reserved capacity, and autoscaling profiles designed up front.

Pipeline automation

Build the MLOps pipeline so experiments are reproducible, promotions are gated, and rollbacks take minutes — not days.

Serving optimization

Tune inference stack for your latency and throughput SLAs — batching, quantization, caching, and load balancing in combination.

Ongoing operations

Drift monitoring, cost reporting, capacity planning, and retraining triggers — operated on retainer or handed off to your team.

Platforms & tools we deploy

vLLMTriton Inference ServerKubeflowRayMLflowFeastEvidentlyKubernetesA100 / H100

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Ready to scale your AI to production traffic?

No SDR, no discovery-call gauntlet. A senior AI infrastructure practitioner personally reviews every submission and replies within one business day.

GPU cluster design & spot strategy

High-throughput inference serving (vLLM)

MLOps pipeline & model registry

Drift monitoring & retraining triggers

Direct contact

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(202) 903-9000