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GPU sharing · Model serving · Inference routing

AI infrastructure on Kubernetes.

We engineer and operate the platform between your models and your accelerators: GPU scheduling and sharing, model serving, inference routing, and the observability and cost control around them.

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  • Open-weight and frontier models
  • Cloud, on-prem, or air-gapped
  • Assess, build, or operate

What it is

The layer your models run on, built for GPUs.

Most platforms weren't built for GPUs, for models, or for the auditors now asking about both. We engineer the Kubernetes layer between your models and your accelerators, then run it for you or hand it over to your team, with one engineering partner accountable for how the pieces work together and no vendor incentive behind any recommendation.

Use cases

Run models on your own terms.

Teams usually come to us with one of two goals. Underneath, it's the same platform: GPUs shared well, models served fast, and every request under your policies.

Run it yourself

Open-weight LLMs on your own servers

Models such as Llama, Qwen, DeepSeek, or Mistral, served on GPUs you control, in your data center or your own cloud account. Prompts and data stay inside your network, cost follows your hardware instead of per-token pricing, and a model changes only when you decide.

  • Serving engines tuned to your models, context lengths, and latency targets
  • Shared GPUs, so several models and teams fit on the cards you already have
  • Your fine-tunes served as LoRA adapters on shared base models

Mix and route

Frontier and local models, behind one gateway

Keep sensitive or high-volume requests on your own models, and send the ones that need a frontier model such as GPT, Claude, or Gemini to its provider. Your applications call one OpenAI-compatible endpoint, and the same authentication, limits, and usage records apply to both.

  • Routing rules by data sensitivity, cost, or the capability a request needs
  • Failover between your own models and hosted APIs
  • Usage and spend per team, across your GPUs and your API bills

What we engineer and operate

Six disciplines, one platform.

Each is a common reason AI stalls between the demo and production. We work on them together, because a change in one shows up in the others.

  • GPU scheduling & sharing

    Fractional GPUs with enforced limits, MIG where you need hard isolation, and DRA as the native API, so one card can serve several workloads.

    • Measurement first: allocation versus real utilization, per team and per workload
    • Sharing that holds: memory and compute limits enforced per workload, not just requested
    • Placement: queueing and bin-packing, so jobs fit the cards you already have

  • Model serving

    Serving engines tuned to your models and traffic, multi-node replicas where weights outgrow a node, and autoscaling on queue depth instead of CPU.

    • Engine choice and tuning for your models, context lengths, and latency targets
    • Rollouts and rollbacks that stage a new model version into live traffic
    • Autoscaling on queue depth and latency rather than CPU

  • Inference routing & KV cache

    Routing that sends each request to the replica already holding its prompt's KV cache, or with the shortest queue, so shared context isn't computed twice and time to first token stays low.

    • Cache-aware routing: conversations and shared prompts land where their KV cache already is
    • Prefix caching in the engine, so repeated system prompts and documents aren't recomputed
    • KV cache offloading to CPU memory or storage, for long contexts and many users at once

  • Multi-model serving

    Many models and fine-tuned adapters on shared GPUs, behind one OpenAI-compatible endpoint, with each team's traffic routed by model name and held to its own limits.

    • One endpoint for every model and version your teams use
    • LoRA adapters on shared base models, instead of a full copy per fine-tune
    • Per-team limits on requests and tokens, so one workload can't starve the rest

  • Observability & GPU cost

    Time to first token, time per output token, and GPU telemetry side by side, and cost by team and model from the same data, so capacity plans follow measured demand.

    • Latency users feel: time to first token and time per output token, per model
    • GPU telemetry: utilization, memory, and errors, from the device up
    • Cost by team and model, with chargeback your finance team can review

  • Sovereign & air-gapped

    Mirrored registries, offline model stores, signed artifacts, and admission policy, for AI that has to stay inside your boundary.

    • Mirrored registries and offline model stores inside your network
    • Signed artifacts, verified before anything runs
    • Admission policy that keeps workloads inside the rules you set

Open source

The cloud native AI stack we build on.

Open-source projects and GPU features, assembled into one platform and operated as one. These are typical choices, not a fixed stack: we select around your requirements and the tools you already run.

Your applications

Chat · agents · APIs

Yours

  1. Gateways & routing

    One OpenAI-compatible endpoint for every model, self-hosted or hosted, with each request routed on cache and queue state.

    • Gateway API Inference Extension
    • llm-d router
    • Agent Routerformerly Envoy AI Gateway

  2. Distributed inference & KV cache

    Inference spread across GPUs and nodes, with KV cache reused across requests instead of recomputed.

    • llm-d
    • LMCache

  3. Model serving

    The engines that run your models, and the Kubernetes resources that deploy, scale, and roll them out.

    • vLLM
    • SGLang
    • KServe

  4. GPU sharing & scheduling

    Fractional and partitioned GPUs, Kubernetes device claims, and job queues with quotas per team.

    • HAMi
    • DRA
    • MIG
    • Kueue

  5. GPU nodes

    Drivers, device plugins, and GPU health monitoring on every node.

    • NVIDIA GPU Operator
    • AMD GPU Operator

Your accelerators

NVIDIA · AMD · cloud · on-prem

Yours

Across every layer

Observability & cost

Latency, GPU telemetry, and cost by team and model.

  • Prometheus
  • OpenTelemetry
  • DCGM Exporter
  • OpenCost

Supply chain & policy

Mirrored images and models, signed artifacts, and admission rules.

  • Harbor
  • Kyverno
  • Sigstore

Where we start

Four situations AI teams bring us.

Each maps to a defined engagement, and each starts with a conversation with an engineer.

"Our GPUs are fully allocated and mostly idle."

Whole-card requests lock up accelerators that do a fraction of the work they could. The review measures real utilization and identifies where sharing, queueing, and bin-packing would recover capacity; putting them in place is a separate Implementation Project, so you recover capacity before you buy more.

Platform Readiness Review

"The model works in the demo. Production is another story."

Latency targets, autoscaling, rollouts, and routing that understands the KV cache: we build the serving platform under your models and hand it over documented, yours to run.

Implementation Project

"Our AI has to stay inside our boundary."

Regulated data, internal models, or a network with no route out. We build the platform with mirrored registries, offline model stores, signed artifacts, and admission policy, so it runs where your rules say it must.

Implementation Project

"Running it is a full-time job we can't staff."

GPU drivers, operators, serving engines, and models all move fast, and one upgrade can break another layer. Our engineers operate, upgrade, and improve the platform under a 12-month partnership, so your team can focus on the models.

Managed Platform

Frequently asked

What AI teams ask before they start.

Do you provide GPUs or models?

No. The models and the accelerators stay yours, in your cloud accounts or your data centers. We engineer and operate the platform between them: how workloads are scheduled and share GPUs, how models are served and routed, and how all of it is observed, costed, and secured.

Can we keep using frontier models like GPT or Claude?

Yes. Many teams run open-weight models on their own GPUs for sensitive or high-volume work, and keep a hosted frontier model for the requests that need it. We put both behind one gateway, so your applications call a single endpoint, and the same authentication, limits, and usage records apply to every request.

Which accelerators and environments do you work with?

NVIDIA and AMD GPUs, on AWS, Google Cloud, Azure, Naver Cloud, or on-premises, including air-gapped networks that run from mirrored registries and offline model stores.

Do we have to replace the tools we already run?

No. We weigh your requirements, existing investments, team skills, and costs, explain the trade-offs, and keep the tools you already run when they fit. No cloud, distribution, or software-vendor incentive sits behind a recommendation.

Do you build or train our models?

No. Your teams own the models and the applications that use them. Our work is the platform underneath: how models are served, routed, and scheduled on GPUs, and how the whole stack is observed and secured.

Where do we start?

With a conversation with an engineer. If you already run AI workloads on Kubernetes, a Platform Readiness Review gives you a fixed-fee, prioritized read on the platform in two to four weeks. If you are starting from scratch, an Implementation Project builds the platform, and a Managed Platform engagement can include the build.

From our engineers

Articles and talks on this work.

Written and presented by the engineers who do this work, so you can judge the depth before you talk to us.

Models, GPUs, and the platform between them.

Talk to the engineers who would build it.

Discuss your AI platform

Opens a short form. An engineer replies within 1–2 business days to talk through where to start.