RUK-COM / NVIDIA + AMD INFRASTRUCTURE

AI-scale compute.
Infrastructure expertise.
Built around you.

From GPU Compute to dedicated servers and clusters, Ruk-Com helps select NVIDIA or AMD around your models, software and budget. We engineer compute, networking and storage together, with installation, validation and agreed operational support.

Plan your GPU solutionExplore cluster architecture
NVIDIA + AMD Cluster Engineering Ruk-Com Experts

TWO ECOSYSTEMS / ONE EXPERT PARTNER

The right platform for the workload.
Expertise across the whole system.

Both NVIDIA and AMD serve a range of AI workloads. Model size, memory, software compatibility and scaling shape the choice. Ruk-Com helps assess requirements and validate a proof of concept before finalizing the configuration.

NVIDIARACK-SCALE AI

NVIDIA GB300 NVL72

A liquid-cooled rack-scale platform with 72 Blackwell Ultra GPUs and 36 Grace CPUs connected by NVLink within the rack. A platform to evaluate for large-model training, reasoning and inference at infrastructure scale.

NVLink / Scale-out

NVLink connects GPUs within the rack domain. Inter-rack communication uses a separately engineered scale-out fabric.

CUDA / NCCL

Validate frameworks, kernels and collectives against the CUDA stack, alongside rack-level power and liquid cooling.

NVIDIA platform reference →
AMDLOCAL AI / WORKSTATION

AMD Instinct MI350P

MI350P is a PCIe GPU accelerator; Threadripper PRO is the host CPU. AMD’s Threadripper Halo Station demonstrates this combination for local AI development, fine-tuning and inference.

Workstation reference

Halo Station is an AMD prototype used here as a system reference, not an announcement of rental or retail availability.

ROCm / HIP / RCCL

Validate models, frameworks and GPU support matrices, including custom kernels and CUDA migration requirements, before selecting the platform.

AMD Halo Station reference →
Ruk-Com platform advisory

We compare proof-of-concept results for your workload: throughput, latency, memory and total cost, then align networking, facilities and support with the chosen platform. Hardware availability and provisioning lead times are confirmed per project.

THREE WAYS / ONE ENGINEERING PARTNER

Your workload.
Your deployment model.

Start with the resources you need or design infrastructure for an entire team. We help define the rental model and operational responsibilities before deployment.

01 / GPU COMPUTE

GPU Compute

GPU resources for your workload

Run training, fine-tuning or inference with resources allocated under your proposal. A fit for teams that want GPU access with infrastructure preparation handled by Ruk-Com.

  • Size GPU, CPU, RAM and storage
  • Define environment and access boundaries
  • Match the runtime to your model
Resource allocation

Resource sizing and isolation are confirmed before use

Discuss this solution
02 / DEDICATED SERVER

Dedicated Server

A dedicated machine, expertly configured

For organizations needing a dedicated server and control over their software stack. Match the system configuration to your model and long-term operations.

  • Dedicated server per agreed configuration
  • OS, driver and GPU runtime installation
  • Network, storage and monitoring setup
Server boundary

Define system access and software ownership clearly

Discuss this solution
03 / DEDICATED CLUSTER

Dedicated Cluster

Multiple servers. One engineered cluster.

Engineer GPU workers, compute fabric and shared storage as one system for distributed training and enterprise AI platforms.

  • Topology and inter-node fabric design
  • Scheduler integration with GPU workers
  • Collective and end-to-end workload validation
Cluster boundary

Plan capacity, networking and expansion together

Discuss this solution

Pricing, GPUs per server, memory, storage, networking and rental terms are specified in each solution proposal.

CLUSTER ARCHITECTURE / FOLLOW THE DATA

Powerful GPUs need
an equally capable system.

Explore three workflows: dataset loading and GPU collectives, inference requests and responses, and checkpoint saves. Compute, storage and management remain distinct.

Client / AI team
SDK · API · Job submission
Service endpoint
Inference request / response
GPU WORKER 01
Same-platform GPU group
GPU↔GPU
Platform interconnect
COMPUTE FABRIC
Between GPU groups
GPU WORKER 02
Same-platform GPU group
GPU↔GPU
Platform interconnect
SHARED STORAGE
Datasets · Models · Checkpoints
Dataset / CheckpointGPU collectiveInference requestInference response

Logical flow for a same-platform cluster. Workers represent compute groups, not a rack or server specification. Fabric and protocols depend on the hardware and validation.

Inside the platform

GB300 NVL72 uses NVLink within its rack domain; MI350P connects over PCIe according to host topology. Communication must be designed and validated for the selected platform.

Between GPU workers

For expansion across servers or racks, evaluate scale-out fabrics such as Ethernet/RDMA or InfiniBand against platform and collective-library compatibility. Different GPU vendors are not combined in one collective group.

Data & control separation

Separate storage traffic and management access in the design to control capacity, access policies and troubleshooting.

ENGINEERED / INSTALLED / VALIDATED

From hardware to a platform
your team can put to work.

Ruk-Com connects every layer: rack, OS and drivers, networking, runtimes and workload validation, with configuration records and operational runbooks.

OS & GPU enablement

Install the OS and vendor drivers. Validate firmware, CUDA for NVIDIA or ROCm/HIP for AMD, and the container runtime, with a documented compatibility matrix.

Network & cluster integration

Design IP addressing, VLANs, routing and fabric connectivity. Validate paths between GPU workers and storage.

Scheduler & orchestration

Design scheduling with Kubernetes or Slurm to suit the workload, including user access, quotas and job submission.

Data pipeline & recovery

Plan dataset, model and checkpoint storage. Define retention, backup and restore procedures separately from the training loop.

Workload validation

Validate GPU health, burn-in and collectives with NCCL for NVIDIA or RCCL for AMD where supported. Benchmark the data pipeline and real model against agreed PoC criteria.

Secure operations

Define administrative access, secrets handling and change windows, with monitoring, alerts and engineer escalation.

Software, licensing, integrations and support levels are selected per workload and proposal; they are not automatically included in every solution.

RUK-COM DATACENTER / COLOCATION

Already own the hardware?
Colocate it. Connect the cluster.

Go beyond rented resources with colocation for your GPU servers at Ruk-Com Datacenter. Our team assesses power, cooling, racks and networking as part of one deployment plan.

Explore Ruk-Com AI DatacenterPlan your colocation
RUK-COM DATACENTERGPU DEPLOYMENT / FACILITY READINESS
Rack & Power

Assess dimensions, weight and power for the actual configuration.

Cooling design

Validate cooling requirements and facility interfaces for the server model.

Private connectivity

Plan connectivity for the cluster, storage and enterprise systems.

Installation & Remote Hands

Install, cable, label and coordinate onsite work under the agreed service scope.

Engineer-led. AI-assisted.

Build observability and runbooks with Ruk-Com engineers. Evaluate Ruk-Com Agent for signal analysis and operational prioritization within defined permissions and approval controls.

Ruk-Com Agent

WORKLOAD FIT / BUSINESS OUTCOMES

Infrastructure for AI
with real work to do.

Start with model size, datasets and workload targets, then define the right GPU, network and storage configuration.

Training & fine-tuning

For teams developing models or adapting them to enterprise data. Evaluate memory, batch size and iteration time.

Inference & private AI

Build a serving stack for APIs, assistants and private enterprise AI. Evaluate latency, concurrency and data access.

Research & AI platforms

Structure a multi-team cluster around queues, projects and resource policies, with utilization data for capacity planning.

DELIVERY / EVIDENCE AT EVERY STEP

One engineering partner.
From design to handover.

Give your GPU investment clear scope, ownership and acceptance criteria, with evidence your team can use to operate the system.

01

Discover & size

Review workloads, models, datasets and access requirements.

DELIVERABLEWorkload profile & resource sizing
02

Design & configure

Confirm hardware, networking, storage and service boundaries.

DELIVERABLEAgreed topology & configuration
03

Install & validate

Install, integrate and test against acceptance criteria.

DELIVERABLETest evidence & performance baseline
04

Handover & operate

Hand over documentation, access and operational contacts.

DELIVERABLERunbook & support ownership

BEFORE YOU DEPLOY

Clarity before
your GPU deployment.

We start with your requirements and define service boundaries around the real workload.

How do GPU Compute, dedicated servers and clusters differ?

GPU Compute focuses on resources allocated to a workload. A dedicated server provides a machine-level boundary; a dedicated cluster spans multiple servers, fabric and supporting systems. Isolation, permissions and resource sizing are confirmed in the proposal.

How do we choose GB300 NVL72 or AMD MI350P?

Start with workloads, model memory and the software stack, then validate suitable configurations through a PoC. GB300 NVL72 is a rack-scale platform; MI350P is a PCIe GPU accelerator, and Halo Station remains a prototype. Available models, capacity and lead times are confirmed before ordering.

Can you configure CUDA, ROCm, Kubernetes or Slurm?

We assess and configure the platform-specific stack: CUDA/NCCL for NVIDIA and ROCm/HIP/RCCL for AMD. GPU, OS, framework, container runtime and scheduler support are checked, with versions, licensing and ownership defined. CUDA workloads are not assumed to migrate to AMD without validation.

Does every cluster require InfiniBand?

Not always. Collective patterns, adapters, switches, software compatibility and budget all matter. The team designs and validates a fabric appropriate to the actual configuration.

Does training continue immediately after a server failure?

It depends on the framework, scheduler and checkpoint strategy. State persistence and restart or restore procedures must be designed explicitly; this does not imply automatic, uninterrupted GPU workload migration.

Can we colocate our own GPU servers?

Yes. The team assesses server models, power, cooling, rack requirements and networking, then defines installation, remote hands and coordination responsibilities.

NVIDIA + AMD / RUK-COM ENGINEERING

Bring your AI ambitions.
Let’s engineer the infrastructure.

Share your model, dataset, user volume or training-job size and preferred deployment model. Ruk-Com will help size GPU, networking, storage and installation as one solution.

Talk to our GPU teamExplore Datacenter & Colocation