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Managed infrastructure for LLM inference and fine-tuning

Closed your round, bought the GPUs, and now the inference bill is bleeding faster than the product ships? Your ML team can train models but doesn't want to get up at 3 AM for vLLM, OOMs and Triton? We take the inference layer off your hands: autoscaling, tracing, cost-per-token. Your researchers stop being on-call.

Our stack is already built: vLLM and TensorRT-LLM for serving, Triton for multi-model endpoints, NVIDIA H100 / A100 as the baseline, Ray and Kubeflow for distributed fine-tuning. For your workload we tune continuous batching, kv-cache offload to NVMe and tensor parallelism sized to the model - so the GPUs stay busy instead of burning money idle.

We bring hardware via our supplier contracts for H100/H200/B200, or we operate on top of your cloud account. Supply window for a dedicated GPU pool: 7-14 days depending on region and card model.

[ Get a 48h deployment plan ]

stack we operate

This is the AI / LLM subset, and the platform layer is identical across ICPs.

AI / LLM: vLLM Triton TensorRT-LLM NVIDIA H100 / A100 Ray Kubeflow

Platform: Kubernetes Terraform Ansible Prometheus Grafana Loki OpenTelemetry PagerDuty

what we deploy

Concrete deliverables for AI / LLM teams, and each ships end-to-end with repo, IaC and runbooks.

[ vLLM inference cluster ] →

H100 / A100 with continuous batching, paged attention, autoscaling on queue depth and p95 TTFT, request tracing via OpenTelemetry.

[ Multi-model serving on Triton ] →

Several models behind one endpoint: dynamic batching, model-mesh for cold start <5s, A/B routing by header.

[ TensorRT-LLM build pipeline ] →

Compile per-GPU (FP8, FP16, AWQ-quant), per-case benchmarks for latency and throughput, artifact cache in S3.

[ Distributed fine-tuning on Ray + Kubeflow ] →

Multi-node DDP / FSDP / DeepSpeed-ZeRO, checkpoint store, auto-retry on preempt, GPU-utilization dashboards.

[ Cost-per-token dashboard ] →

Per-model, per-tenant, per-region breakdown. Alerts on budget burn, savings recommendations across the spot/on-demand mix.

what we operate 24/7

After handoff the pager lives with us, and we tune the coverage for LLM workloads:

GPU health watchdog: ECC errors, thermal throttling, driver xid signals trigger preempt and load migration.
Auto-recovery from OOM: batch-size shrink, kv-cache eviction, model-version rollback playbook.
p95 / p99 latency SLO per endpoint: alert on drift >15% off baseline over 5 min.
Cost-per-token alerts: if actual cost climbs >10% over 24h, on-call engineer investigates before closing the ticket.
Versioned runbooks: model rollback, region traffic shift, deflake a flapping endpoint.
Monthly perf review: fresh benchmarks, updated spot/on-demand mix, batching recommendations.

migration scenarios

Here's what we move without downtime for production inference.

spot-fleet to dedicated H100

Inference shifted off spot onto dedicated H100/H200: typical 60% reduction in cost per token, p99 latency stabilizes.

OpenAI proxy to in-house inference

Offload traffic from a managed API onto your cluster: shadow mode, gradual per-tenant cutover, fallback to the proxy on incident.

cloud to bare-metal

GPU fleet moved from AWS p4d/p5 onto bare metal at Latitude.sh / DataPacket: 40% cost reduction, controlled artifact sync.

FP16 to FP8 / quantization

Model rebuild to FP8 or AWQ: 2x VRAM reduction, quality benchmark (perplexity, harness metrics) at each step.

single-region to multi-region

Inference split across 3+ regions for latency and failover: geo-routing, model replication, tenant-sticky sessions.

engine swap (TGI to vLLM)

Parallel shadow inference, output-matching quality control, gradual traffic cutover by cohort.

cases

Anonymized. NDAs cover names; the numbers are real.

LLM startup · 4 mo · vLLM cluster across 3 regions · cost / token: -60% · p95 TTFT: 180 ms

Voice-AI product · 8 mo · 24 H100, multi-model Triton · 99.96% uptime · 10x autoscale at peak

B2B copilot · 6 mo · fine-tune pipeline + serving · time-to-experiment from 3 days to 4 hours

Research lab · 3 mo · 64 A100 spot fleet · 0 lost checkpoints over the quarter

SLA tiers

Three coverage levels. For production inference with user traffic we recommend Silver or higher: an OOM at 3 AM won't wait until morning.

Tier	Response p95 (Sev-1)	Coverage	Incident report	Engineer hours / mo
Bronze	30 min	Business hours, 5×8	Within 48h	40
Silver	15 min	24/7 on-call rotation	Within 24h	80
Gold	5 min	24/7 with dedicated engineer	Within 12h	160+

FAQ

We already bought H100s. What do you do?

We come in on top of your hardware as a DevOps team: we bring up Kubernetes on GPU, MIG partitions, scheduling, and observability. In 2-3 weeks you have an inference cluster, and in 4 you have a signed SLA with 24/7 coverage. Provider billing stays with you.

Can you help source GPUs? Demand is brutal.

Yes. We have open relationships with Latitude.sh, DataPacket, OpenMetal, and regional bare-metal operators, so the supply window for H100 / H200 is usually 7-14 days, and spot access to A100s we can usually find within 72h. Send the spec, and we'll reply with a concrete window in 24h.

Which models do you support in serving?

Any open-weight transformer via vLLM or TensorRT-LLM: Llama, Qwen, Mistral, Mixtral, DeepSeek, Phi, Gemma. Custom architectures we bring up via the Triton Python backend, and audio / vision models via Triton with an ensemble config.

What about fine-tuning? Do you train models for us?

We run the infrastructure for fine-tuning, not the fine-tuning itself - so that means a distributed cluster on Ray / Kubeflow, a checkpoint store, retry mechanics, and GPU-utilization dashboards. The actual ML work (LoRA / SFT / DPO recipes) stays on your side, but if you need an ML expert, we can bring in a partner.

What cost-per-token guarantees do you offer?

We don't sign a fixed number, since it depends on the model, context length, and batching. What we do sign is a per-tenant cost dashboard, a monthly perf review with concrete optimizations, and alerts on >10% drift. Typical economics: a 40-60% cost reduction in the first 2 months via batching, quantization, and a spot/on-demand mix.