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AI INFRASTRUCTURE / COMPUTE / DATA / NETWORK

Infrastructure built around the workload.

We design AI platforms by matching accelerators, memory, networking, storage and software to the actual machine-learning lifecycle.

From NVIDIA B200 and B300 systems to RTX PRO, AMD Instinct and Intel Gaudi platforms, klimko.tech develops balanced infrastructure concepts for training, inference, visual AI, scientific computing and enterprise deployment.

Explore Compute Platforms Discuss Your AI Platform

ACCELERATOR STRATEGY

Different processors for different AI economics.

There is no universal “best GPU.” The right platform depends on model size, precision, memory footprint, software stack, utilization target and scale.

NVIDIA / 01

B200

High-end Blackwell platform for large-scale training and demanding generative AI inference.

ClassDatacenter SXM
FocusTraining + Inference
InterconnectNVLink / Scale-up
DeploymentHGX / DGX
Best fit
Foundation modelsMultimodal AIHPC
NVIDIA / 02

B300

Blackwell Ultra platform designed for very large models, reasoning workloads and dense production inference.

System memory2.1 TB / 8-GPU DGX
NetworkUp to 800 Gb/s
FocusReasoning AI
DeploymentDGX / HGX
Best fit
Large contextMoEHigh-throughput inference
NVIDIA / 03

RTX PRO

Flexible Blackwell PCIe platform for enterprise AI, visual computing, video, rendering and smaller-scale inference.

Memory96 GB GDDR7
Form factorPCIe
FocusAI + Graphics
DeploymentServer / Workstation
Best fit
Vision AIDigital twinsMedia
AMD / 04

Instinct MI350

Large-memory accelerator platform for training, inference and HPC with a strong open-software positioning.

MemoryUp to 288 GB HBM3E
BandwidthUp to 8 TB/s
ArchitectureCDNA 4
SoftwareROCm
Best fit
Large memory modelsHPCOpen stack
AMD / 05

Instinct MI300X / MI325X

Memory-rich alternatives for transformer workloads, scientific computing and large batch inference.

MI300X192 GB HBM3
MI325X256 GB HBM3E
FocusTraining + Inference
Scale8-GPU platforms
Best fit
Memory-bound AILLM servingResearch
INTEL / 06

Gaudi 3

AI accelerator with integrated high-speed Ethernet and a software path centered on PyTorch and open frameworks.

MemoryUp to 128 GB HBM2e
BandwidthUp to 3.7 TB/s
NetworkIntegrated Ethernet
FocusTraining + Inference
Best fit
Ethernet clustersPyTorchCost alternatives

PLATFORM SELECTION

Choose for the workload, not for the headline benchmark.

Accelerator choice must consider model memory, training precision, inference latency, batch size, software maturity, cluster topology, power and cooling.

Vendor benchmark data is useful for orientation, but final architecture should be validated against the customer’s models, dataset and deployment constraints.

Workload
Preferred class
Main reason
Key risk
Large foundation-model training
B200 / B300 / MI350
Scale-up memory and interconnect
Power, cooling, software fit
High-volume LLM inference
B300 / B200 / MI350
Memory capacity and token throughput
KV cache and storage latency
Visual AI and digital twins
RTX PRO
AI plus graphics and video
PCIe scale-up limits
Open software / alternative stack
AMD Instinct
ROCm and large HBM capacity
Application validation
Ethernet-native AI cluster
Intel Gaudi 3
Integrated Ethernet architecture
Framework and model support

MACHINE LEARNING PLATFORM

Compute is only one layer of the system.

A production AI platform must support ingest, data preparation, training, validation, model registry, inference and continuous monitoring.

We design the infrastructure around the complete ML lifecycle, including scheduling, isolation, observability, data access and future scale.

Training clustersInference clusters Kubernetes / SlurmModel registry Data pipelinesObservability MLOps integrationCapacity planning

HIGH-PERFORMANCE DATA

Keep accelerators fed with the right storage architecture.

AI storage must handle concurrent random reads, high-throughput sequential writes, checkpoints, metadata pressure and large-scale inference data access.

01 / TRAINING

Parallel File Systems

Distributed data access for many compute nodes reading and writing concurrently.

02 / GPU PATH

GPUDirect Storage

Direct storage-to-GPU data paths can reduce CPU overhead and improve latency for data-intensive workloads.

03 / MEDIA

NVMe & NVMe-oF

Low-latency flash tiers for active datasets, checkpoints and performance-sensitive inference.

04 / LIFECYCLE

Object & Archive

Scalable capacity for raw data, versioned datasets, model artifacts and long-term retention.

Dataset profileFile count, object size, read pattern and write behavior drive the design.
Checkpoint strategyTraining recovery objectives determine write bandwidth and capacity.
GPU utilizationStorage and network bottlenecks can leave expensive accelerators idle.
Data governancePerformance must coexist with security, lifecycle and compliance controls.

VENDOR LANDSCAPE

Technology selected by role inside the architecture.

We use vendor reference architectures and performance data as inputs, then validate the complete concept against the real workload and site constraints.

NVIDIAB200, B300, RTX PRO, DGX, HGX, networking and AI software.
AMDInstinct MI300, MI325 and MI350 accelerator platforms.
IntelGaudi accelerators and Xeon host platforms.
SupermicroGPU server and rack-scale system integration.
Dell TechnologiesEnterprise compute, storage and integrated infrastructure.
HPEEnterprise AI and HPC infrastructure platforms.
LenovoGPU compute and liquid-cooled infrastructure options.
WEKAParallel AI storage, NVMe and GPUDirect Storage architectures.
VAST DataUnified high-performance AI data platform.
DDNAI and HPC storage for large GPU environments.
Pure StorageFlashBlade platforms and enterprise AI data architectures.
NetAppEnterprise data management, hybrid cloud and AI data services.
AristaHigh-performance Ethernet fabrics for scale-out AI.
Juniper NetworksData center switching and routed AI fabrics.
NVIDIA NetworkingInfiniBand, Spectrum Ethernet, ConnectX and BlueField.

DISCUSS YOUR AI PLATFORM

Need a balanced compute, network and storage concept?

Share the models, dataset size, expected users, training or inference profile, deployment location and growth targets. We will help define the right architecture.

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