AI infrastructure

GPU Cloud

GPU infrastructure for training and serving models, built around your workloads. We stand up clusters, schedulers and inference platforms that keep expensive hardware busy and your costs under control.

Discuss your project See how it works

70 to 90%GPU utilisation with proper scheduling, up from 20 to 40

Secondsto schedule a job onto a free GPU with a queue in place

30 to 60%lower spend using spot capacity and right sizing

Any cloudAWS, GCP, Azure, or dedicated hardware you own

The economics of GPUs

The hardware is expensive. Idle hardware is the real cost.

A GPU that sits unused overnight, or a training run that grabs eight cards and uses two, quietly burns a large budget. Most AI teams run their accelerators at a fraction of capacity because there is no scheduler, no queue and no visibility.

We build the layer that makes GPUs a shared, measured resource: a queue that packs jobs onto free cards, fair sharing between teams, autoscaling that follows real demand and dashboards that show cost per experiment.

Whether you burst to a cloud provider, run a fleet of your own machines or do both, the goal is the same: researchers get a GPU in seconds, and finance gets a bill that makes sense.

Cluster and scheduling

Turn a pile of GPUs into a shared pool

A scheduler accepts jobs with resource requests and priorities, queues them and places them on free hardware, bin packing so cards are not stranded. Interactive sessions, batch training and inference all share the same pool with the right guarantees.

Teams get quotas and fair share, so one large sweep does not starve everyone else, and preemption lets low priority work use spare capacity safely.

Queueing with priorities, quotas and fair share
Gang scheduling for multi GPU and multi node jobs
Preemption and checkpointing for spare capacity use
Node pools for different GPU types and price points

01Scheduler capabilities

Placement: Bin packing, topology aware, NUMA aware
Sharing: Time slicing and MIG for small jobs
Fairness: Per team quota, borrow and reclaim
Resilience: Auto requeue, checkpoint restore

Distributed training

Scale a run across many GPUs without the pain

We set up data and model parallel training with the right communication backend, fast interconnect and storage that can feed the GPUs, so scaling from one card to many actually speeds things up.

Runs are checkpointed and resumable, so a preempted or failed job continues rather than starting over.

Data, tensor and pipeline parallel strategies
NCCL over InfiniBand or high speed Ethernet
Sharded checkpointing to object storage
Throughput profiling to find the real bottleneck

02Training infrastructure

Interconnect: InfiniBand, EFA, RoCE
Data: Parallel file system or cached object store
Frameworks: PyTorch DDP and FSDP, DeepSpeed, Megatron
Recovery: Elastic launch, checkpoint every N steps

Inference serving

Serve models fast, and only pay for what you use

An inference platform with batching, caching and autoscaling keeps latency low under load and scales to zero when traffic stops. For language models we use servers built for high throughput token generation.

Multiple models share GPUs where it is safe to, and traffic can be split for canary releases and A/B tests.

Dynamic batching and response streaming
Scale to zero and scale out on real demand
GPU sharing across models with isolation
Canary and shadow deployments with metrics

03Serving stack

Runtimes: vLLM, TensorRT LLM, Triton, TGI
Autoscaling: Queue depth and latency driven
Routing: Weighted, canary, shadow
Efficiency: KV cache reuse, quantised weights

What a Spykra GPU platform includes

The controls that keep utilisation high, costs visible and researchers unblocked.

Cost and usage reporting

Spend and GPU hours broken down by team, project and experiment, with idle capacity and oversized jobs surfaced.

Spot and reserved mix

Interruptible capacity for checkpointed training, reserved or owned hardware for steady inference, chosen automatically by workload.

Utilisation dashboards

Real time view of every GPU, what is running on it and whether it is actually busy or just allocated.

Isolation and access

Per team namespaces, network policy, secrets management and audit logs for a shared but secure cluster.

Reproducible environments

Container images and environment definitions per project, so a run behaves the same on any node.

Health and alerting

GPU temperature, ECC errors, throttling and job failures monitored, with unhealthy nodes drained automatically.

Who this is for

Teams that have outgrown running notebooks on a single instance.

AI product teams

A shared cluster for training and a serving platform for the models behind your features, with cost per feature visible.

Research groups

Fair sharing of GPUs across many researchers, with queues, quotas and quick interactive access.

Model fine tuning at scale

Multi node runs for fine tuning and continued pretraining, checkpointed and resumable on spot capacity.

Enterprises consolidating spend

One platform across departments to replace scattered instances, with chargeback and governance.

Batch inference pipelines

Large offline scoring jobs packed onto spare capacity overnight at the lowest possible cost.

Regulated workloads

GPU infrastructure on dedicated or on premise hardware where data residency and isolation are mandatory.

How the platform is built

Running jobs within weeks, then tuned for utilisation and cost.

Profile the workloads

We look at your training and inference patterns, current spend and pain points, then choose cloud, dedicated or hybrid and the scheduler.

Stand up the cluster

Provisioning, networking, storage, the scheduler and a first set of node pools, with a sample job running end to end.

Onboard the teams

Quotas, environments, CI for images and self serve access, so researchers submit jobs without tickets.

Tune and operate

We drive utilisation up with scheduling and spot strategy, report cost per team and run the platform or hand it to your infra group.

Infrastructure and tools

Orchestration

KubernetesSlurmRayVolcanoKueueRun:ai patterns

Training and serving

PyTorch FSDPDeepSpeedvLLMTensorRT LLMTritonNVIDIA NeMo

Platform

AWSGCPAzureTerraformPrometheusGrafana

Questions about GPU infrastructure

We just run on one big cloud instance. When do we need this?

When more than a couple of people are competing for GPUs, when runs are being killed or queued informally over chat, or when the bill has grown faster than the work. A scheduler and cost reporting usually pay for themselves quickly at that point.

Cloud GPUs or our own hardware?

It depends on steady state demand. Bursty or early stage work suits the cloud. Once you have a predictable baseline of GPU hours, owned or long term reserved hardware is often far cheaper. Many teams run a hybrid, and we build for that.

How do you cut GPU cost without slowing us down?

Higher utilisation through scheduling, right sizing jobs that over allocate, spot capacity for anything checkpointed, scale to zero for spiky inference, and quantised serving. Each is measured so you see the saving.

Can you serve large language models efficiently?

Yes. We use serving engines built for token generation with continuous batching and KV cache reuse, plus quantisation where quality allows, which raises throughput per GPU substantially compared with a naive setup.

Who runs the platform afterwards?

Your choice. We can operate it under a support agreement, or hand it to your infrastructure team with documentation, runbooks and a handover period. Everything is standard open tooling, so you are not tied to us.

Send us your GPU bill and your backlog

We will show you where the utilisation is going and what a properly scheduled platform would change.

Start a project Spykra Technologies UK Ltd, London and Mumbai.