AI & MLOps Infrastructure

AI infrastructure that actually works

Your model works in the notebook. Now it needs to work in production — at scale, with governance, without burning through your GPU budget.

We build and operate the GPU-enabled Kubernetes platforms that take AI from experiment to production.

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$340K
Monthly GPU savings achieved
94%
GPU utilization (from 40%)
3x
Faster model training cycles
90%
Faster inference deployment

Why Choose THNKBIG for AI Infrastructure and MLOps

THNKBIG is a US-based AI infrastructure consulting firm with offices in Texas and California, specializing in GPU-optimized Kubernetes platforms for machine learning workloads. We bridge the gap between data science and production operations, helping organizations move from notebook experiments to scalable inference systems.

Our MLOps consulting expertise spans the full ML lifecycle: data pipelines, distributed training infrastructure, model serving with KServe and Triton, and the governance frameworks enterprises require. We understand GPU economics deeply, implementing scheduling policies and spot instance strategies that routinely deliver 30-50% cost reductions.

Organizations partner with THNKBIG to accelerate their AI initiatives without burning through GPU budgets. We build platforms that let your data scientists focus on models while your operations team gains the observability and control needed to run ML workloads in production confidently.

The model isn't the hard part

Your data scientists built something remarkable in a Jupyter notebook. Now someone has to make it run in production — reliably, at scale, within budget, with governance. That's where most AI initiatives stall.

87%

of ML models never reach production

60%

of ML team time spent on infrastructure

40%

average GPU utilization (industry)

The Platform

Production ML infrastructure, not science projects

We build the complete MLOps platform — from data pipeline to production inference — on Kubernetes infrastructure designed for GPU workloads.

01

Data Pipeline

High-throughput data ingestion and preprocessing at scale. We architect pipelines that handle petabytes without breaking your budget.

Distributed data processing Feature stores Data versioning Pipeline orchestration
02

Training Infrastructure

GPU-optimized Kubernetes clusters designed for ML training workloads. Multi-node distributed training, checkpointing, and experiment tracking.

Multi-GPU scheduling Distributed training Experiment tracking Model registry
03

Model Serving

Production inference at scale with KServe, Triton, or custom serving solutions. Auto-scaling, A/B testing, and canary deployments included.

KServe / Triton Auto-scaling inference A/B testing Model monitoring
04

Operations & Governance

The part everyone forgets: monitoring, alerting, cost controls, and compliance. Your AI platform needs operational discipline, not just notebooks.

Model monitoring Drift detection Cost controls Compliance automation
GPU Optimization

Stop wasting money on idle GPUs

GPU infrastructure is expensive. Most organizations run at 40% utilization or less. We fix that.

Problem

40% average GPU utilization

Solution

Right-sized scheduling and bin-packing

Outcome

94% utilization achieved

Problem

$1.2M/month GPU spend

Solution

Spot instances + preemption handling

Outcome

$340K/month savings

Problem

3-day training jobs failing

Solution

Checkpointing + distributed training

Outcome

4-hour training cycles

Problem

Weeks to deploy a model

Solution

GitOps + KServe automation

Outcome

Same-day deployment

Case Study

Series C AI company cuts GPU costs by $340K/month

Technology

The Challenge

A Series C AI company was spending $1.2M/month on GPU infrastructure with only 40% average utilization. Training jobs took 3 days and frequently failed. Model deployment required manual intervention and took weeks.

Our Approach

  • Audited GPU scheduling and identified utilization bottlenecks
  • Implemented GPU bin-packing and fractional GPU allocation
  • Deployed spot instance strategy with graceful preemption handling
  • Built GitOps pipeline for model deployment with KServe
  • Implemented distributed training with automatic checkpointing

Results

$340K/mo

GPU cost savings

94%

GPU utilization

3x

Faster training

Same-day

Model deployment

FAQ

Frequently asked questions

Your data scientists are experts at building models — not at running Kubernetes, optimizing GPU scheduling, or building production serving infrastructure. We handle the platform so they can focus on the science. Most ML teams we work with spend 60%+ of their time on infrastructure. We flip that ratio.
MLOps adds model-specific concerns: experiment tracking, model versioning, feature stores, training infrastructure, serving infrastructure, model monitoring, and drift detection. A DevOps team that hasn't worked with ML workloads will miss critical requirements around GPU scheduling, data pipelines, and model lifecycle management.
Yes. We work with MLflow, Kubeflow, Ray, Weights & Biases, SageMaker, Vertex AI, and most major ML platforms. We're not here to replace your tools — we're here to make them work together in production.
GPU costs are the #1 concern for every AI team we work with. We implement: right-sized GPU allocation (most teams over-provision), bin-packing to maximize utilization, spot/preemptible instances with graceful handling, time-boxing for training jobs, and idle detection with automatic scale-down. Typical savings: 30-50% of GPU spend.
We've deployed Kubernetes on on-prem NVIDIA DGX clusters, custom GPU servers, and hybrid environments. On-prem adds complexity around GPU drivers, networking, and storage — but it's often the right choice for cost or data sovereignty reasons. We'll help you decide and implement either path.
GPU cost optimization typically shows results in 2-4 weeks — it's often quick wins around scheduling and right-sizing. Full MLOps platform buildout is typically 8-12 weeks to production-ready state. We deliver incrementally, so you're seeing value throughout the engagement.
No. We build the infrastructure that trains models. We're platform engineers, not data scientists. We'll make your ML team dramatically more productive, but we won't build your models for you.

Technology Partners

AWS Microsoft Azure Google Cloud Red Hat Sysdig Tigera DigitalOcean Dynatrace Rafay NVIDIA Kubecost
Proof Point

AI/ML infrastructure at scale

AI/ML Series C

$340K/mo GPU savings for a Series C AI platform

GPU utilization jumped from 40% to 94% after we deployed scheduling policies, spot instance strategies, and real-time cost visibility across their Kubernetes clusters.

Read the full case study →
Insights

Related Reading

The AI Infrastructure Gap: Why Demos Don't Deploy

93% of orgs deploy AI models less than daily. Here's what's missing in the infrastructure layer.

Running GPU Workloads on Kubernetes

A practical guide to GPU scheduling, node configuration, and AI/ML workload orchestration.

THNKBIG Partners with Run:ai for GPU Orchestration

How Run:ai's GPU virtualization platform helps enterprises maximize AI infrastructure ROI.

AI Infrastructure and MLOps Consulting for Production Workloads

Running AI workloads and machine learning infrastructure at enterprise scale requires more than cloud compute — it requires orchestration expertise, GPU scheduling knowledge, and MLOps practices that keep models moving from training to production efficiently. THNKBIG's AI infrastructure consulting practice helps organizations design, build, and operate the Kubernetes-based platforms that power their AI and ML workloads.

Our AI infrastructure consulting covers GPU cluster design on Kubernetes, NVIDIA GPU operator deployment, model serving infrastructure with tools like vLLM and Triton Inference Server, and MLOps pipeline implementation using Kubeflow, MLflow, and Argo Workflows. We work with organizations building AI infrastructure on AWS, Azure, and GCP — as well as on-premises GPU clusters for organizations with data sovereignty or latency requirements. Our team has deployed large-scale AI workloads for enterprises in healthcare AI, financial services analytics, and autonomous systems development.

AI infrastructure is only as effective as the MLOps practices surrounding it. THNKBIG implements end-to-end machine learning infrastructure that connects data pipelines, experiment tracking, model registry, and deployment automation. We help organizations reduce model deployment time from weeks to hours, implement automated retraining pipelines, and establish the monitoring infrastructure needed to detect model drift in production AI workloads.

Ready to make AI operational?

Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.

Schedule an Infrastructure Assessment Call Us Directly

US-based team · All US citizens · Continental United States only