GPU Kubernetes & LLM Inference Consulting
Serve your AI models faster on a smaller GPU bill. We build and tune the model-serving platform so inference stays fast under load — without paying for idle GPUs.
For the platform team: production serving with vLLM, Triton, and KServe. For the CIO and CFO: GPU cost that tracks real traffic. Runs on-prem, hybrid, or air-gapped for regulated data.
Production LLM inference is an infrastructure problem, not a model problem
A model that answers instantly in a notebook can fall over the first time it meets production traffic. LLM inference at scale is about throughput, latency, and GPU cost — and those are decided by the serving runtime, the GPU scheduling, and the autoscaling underneath the model, not by the model weights.
THNKBIG is a US-based platform-engineering consultancy that builds and optimizes GPU Kubernetes inference platforms. We are runtime- and hardware-neutral: we deploy the model serving stack — vLLM, NVIDIA Triton, KServe, or TGI — on the GPUs you run, tuned to your latency and cost targets.
Model serving that keeps GPUs busy
The single biggest lever in LLM inference is keeping the GPU saturated. Continuous batching packs concurrent requests onto each accelerator; quantization and KV-cache reuse fit more model per GPU; token streaming keeps latency low for the user. We configure the serving runtime for your models so throughput goes up and cost per token comes down — often dramatically versus default settings.
GPU scheduling on Kubernetes
Kubernetes GPU scheduling is what turns fixed accelerator cost into the most inference per dollar. Using the NVIDIA GPU Operator, MIG partitioning (on A100/H100) and time-slicing (all GPUs, including L40S), we pack workloads onto fewer accelerators and isolate them so training never starves serving. Demand-based autoscaling — scale-to-floor for synchronous traffic, scale-to-zero for batch and async — means GPU spend tracks real traffic instead of provisioned capacity. This is the difference between a GPU bill that scales with usage and one that scales with fear.
LLM inference optimization against real SLOs
We tune inference against explicit service-level objectives: p95 and p99 latency, tokens per second, and cost per million tokens. Observability on GPU utilization and latency percentiles makes the trade-offs visible, and canary and A/B rollout let you ship model or configuration changes without regressing production. The result is a Kubernetes AI platform your team can operate with confidence — and a serving cost you can forecast.
The GPU Kubernetes inference stack
From serving runtime to GPU scheduling to the cost controls that keep it affordable.
Model serving
vLLM, Triton, KServe, or TGI with continuous batching and token streaming — configured for your models' latency and throughput targets.
GPU scheduling
NVIDIA GPU Operator, MIG partitioning (A100/H100), time-slicing, and bin-packing so accelerators stay saturated and workloads stay isolated.
Inference optimization
Quantization, KV-cache tuning, and right-sizing to hit your p95 latency SLO on the cheapest GPU that carries the load.
Autoscaling & cost
Demand-based autoscaling — scale-to-floor for live traffic, scale-to-zero for batch — plus utilization dashboards, so GPU spend tracks real inference traffic.
Observability
DCGM, Prometheus, and Grafana for GPU utilization, latency percentiles, and tokens/sec — the metrics that matter for serving.
Safe rollout & handover
Canary and A/B rollout, runbooks, and GitOps infrastructure-as-code so your team ships changes and operates the platform independently.
A GPU Kubernetes inference platform we've shipped
Model serving, GPU scheduling, and autoscaling — architected for throughput, latency, and cost.
Inference requests
API gateway · routing · load balancing
Model serving
KServe orchestrating vLLM · Triton · TGI · continuous batching · KV-cache
GPU-scheduled Kubernetes
NVIDIA GPU Operator · MIG · time-slicing · bin-packing
GPU nodes
H100 · A100 · L40S · high-bandwidth interconnect
Cost & Utilization
Right-sizing · autoscaling · scale-to-floor
GPU spend tracks real traffic; scale-to-zero for async and on-demand inference, scale-to-floor for live SLO traffic.
Observability & SLOs
DCGM · p95/p99 latency · throughput
GPU utilization, latency percentiles, and tokens/sec measured against explicit SLOs; canary and A/B rollout for safe releases.
GPU inference in production
Real GPU Kubernetes engagements — from bare-metal buildouts to hardened production inference in six weeks.
Real result · Series C AI platform
$340K
per month saved
94%
GPU utilization (was 40%)
100%
cost visibility
2 wks
to first savings
$1.2M/mo GPU spend at 40% utilization → right-sized with Kubecost, GPU bin-packing, and spot for training jobs. Outcomes vary by environment; full details & client references on request.
Six-Week AI Rapid Strike
A hardened RKE2 cluster with on-prem NVIDIA GPUs serving open models in production — GPU scheduling, model serving, and observability shipped in six weeks.
Read the full case study → On-Prem GPUOn-Prem AI Cluster Buildout
GPU-scheduled Kubernetes on bare metal — model serving and scheduling tuned for throughput and utilization.
Read the full case study →From assessment to a platform you own
Assess & Stabilize
Readiness assessment: current serving stack, latency SLOs, GPU utilization, and cost-per-token baseline.
Build & Harden
Serving runtime, GPU-scheduling, optimization, and autoscaling built and hardened as GitOps infrastructure-as-code.
Operate & Transfer
Observability, runbooks, and knowledge transfer so your team operates the inference platform independently.
Insured · client references and certificate of insurance available on request · Backed by senior, US-based platform engineers — meet the team.
GPU Kubernetes & LLM inference: frequently asked questions
What does LLM inference on Kubernetes actually involve?
LLM inference is the production serving of a trained model — turning a prompt into a response at scale, under latency and cost constraints. On Kubernetes that means a serving runtime (vLLM, Triton, KServe, or TGI) with continuous batching, GPU scheduling via the NVIDIA GPU Operator, autoscaling tied to real demand, and observability on latency and throughput. We build and operate that whole path so your models serve production traffic reliably instead of falling over the first time load spikes.
How do you optimize LLM inference for cost and latency?
LLM inference optimization is mostly about keeping GPUs saturated and picking the cheapest hardware that meets your latency SLO. We use continuous batching to raise throughput, quantization and KV-cache reuse to fit more on each GPU, MIG partitioning (on A100/H100) and time-slicing to share accelerators, and demand-based autoscaling — scale-to-floor for synchronous traffic, scale-to-zero for batch and async — so you stop paying for idle capacity. Teams typically see large drops in cost per token once serving is tuned rather than run at default settings.
Which serving runtimes and GPUs do you support?
We are runtime- and hardware-neutral. For model serving we deploy vLLM, NVIDIA Triton Inference Server, KServe, or TGI depending on your models and throughput needs, on H100, A100, L40S, or whatever GPUs you run — cloud, on-prem, or hybrid. We pick the combination that meets your latency and cost targets rather than pushing a single stack.
How does GPU scheduling on Kubernetes reduce waste?
Most teams run GPU nodes 24/7 even when traffic is intermittent, so accelerators sit idle. Kubernetes GPU scheduling with the NVIDIA GPU Operator, MIG partitioning (on A100/H100), and time-slicing (all GPUs) packs workloads onto fewer accelerators, and autoscaling adds or removes replicas as demand moves. The result is GPU cost that tracks actual inference traffic instead of provisioned capacity.
Is this the same as your AI infrastructure or self-hosted LLM work?
They connect but the focus differs. This page is about the performance and cost of serving AI models — throughput, latency, and GPU efficiency for Kubernetes AI workloads. Our on-prem and self-hosted LLM work focuses on data sovereignty and running private models inside your boundary, and our broader AI infrastructure practice covers the full platform including MLOps pipelines. Many engagements combine them.
Will our team own and operate the inference platform?
Yes. We are vendor-neutral platform engineers, not a managed-service lock-in. Every engagement ends with GitOps-managed infrastructure-as-code, runbooks, and observability so your team runs the GPU Kubernetes inference platform independently. References and a certificate of insurance are available on request.
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.
US-based team · All US citizens · Continental United States only