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Hardware Stack

FarmGPU’s infrastructure is designed as a cohesive system, optimized for AI workloads from silicon through storage and networking. We prioritize performance, efficiency, and forward compatibility, enabling us to deploy the latest hardware while maintaining operational stability.

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GPUs: High-Performance AI Compute

FarmGPU supports a broad range of modern NVIDIA GPUs to address both frontier and applied AI workloads.

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Supported GPU Platforms

  • NVIDIA H100
  • NVIDIA H200
  • NVIDIA B200 (Blackwell)
  • NVIDIA RTX Pro 6000 Blackwell
This range allows us to support:
  • Large-scale model training
  • High-throughput inference
  • Fine-tuning and experimentation
  • Enterprise and workstation-class AI workloads
Our GPU nodes are configured for maximum utilization and predictable performance, with platform designs aligned to real-world AI workload characteristics rather than generic cloud abstractions.

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CPUs: Balanced Host Compute

FarmGPU pairs GPUs with modern, high-performance CPU platforms to ensure that host-side compute never becomes a bottleneck.

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Supported CPU Platforms

  • AMD Turin
  • Intel Granite Rapids
These CPUs provide:
  • High core counts and memory bandwidth
  • Strong PCIe 5.0 connectivity
  • Balanced performance for data preparation, orchestration, and I/O-heavy tasks
By supporting both AMD and Intel platforms, FarmGPU maintains flexibility across supply chains and customer requirements.

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Storage Fabric: AI-Optimized by Design

FarmGPU treats storage as a core system component, not a peripheral service. Our storage architecture is designed to deliver predictable, high-throughput data paths that keep GPUs fed under real AI workloads.

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Storage Software Platforms

We deploy and operate leading, production-grade storage platforms, including:
  • MinIO
  • VAST Data
  • Weka
  • Ceph
All storage platforms are benchmarked and validated using FarmGPU’s internal Silo benchmarking and evaluation suite to ensure consistent performance across training and inference workloads.

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Storage Hardware

  • Solidigm SSDs across all platforms
  • PCIe 5.0 NVMe SSDs in all GPU compute nodes
  • High-density 122TB QLC SSDs in storage nodes for cost-efficient scaling
This combination enables both high-performance hot data paths and economical cold and capacity tiers, optimized for AI dataset growth.

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DPU-Accelerated Data Path (NVIDIA BlueField-3)

A key differentiator in FarmGPU’s storage architecture is the use of NVIDIA BlueField-3 DPUs. BlueField-3 DPUs offload critical infrastructure functions from the host CPU, including:
  • Storage protocol processing
  • Network virtualization
  • Encryption and security
  • Data movement orchestration
By offloading these tasks, FarmGPU enables:
  • GPU Direct Storage (GDS) with near bare-metal throughput
  • Reduced CPU contention on GPU nodes
  • More predictable latency under load
  • Higher effective GPU utilization (MFU)
In AI systems, performance is determined by the end-to-end data path. DPUs allow FarmGPU to optimize that path holistically, rather than relying on CPU-bound software layers.

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Networking: High-Bandwidth, Low-Latency Fabrics

FarmGPU deploys a dual-fabric networking architecture to separate customer-facing traffic from backend AI communication.

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Front-End Network

  • NVIDIA SN5600
Optimized for:
  • North–south traffic
  • Customer access
  • Control plane and service connectivity

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Back-End AI Fabric

  • Celestica DS5000
  • Broadcom Tomahawk 5 ASIC
Optimized for:
  • East–west GPU communication
  • Storage traffic
  • Collective operations (e.g., NCCL)

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Fabric Management

  • Hedgehog Open Network Fabric
Provides:
  • Open, vendor-neutral fabric management
  • Automated provisioning and monitoring
  • High observability and control

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RDMA Support

  • RDMA over Converged Ethernet (RoCE)
  • Tuned with Rocky v2
This networking architecture ensures:
  • Predictable latency
  • High throughput at scale
  • Efficient GPU-to-GPU and GPU-to-storage communication

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Cooling: Today and Tomorrow

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Current Deployment

  • Air-cooled GPU servers deployed in existing and legacy data centers
  • Optimized airflow and rack density based on GPU thermal profiles

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Future Roadmap

  • Partnerships with two major immersion cooling providers
  • Initial immersion deployments planned for 2026
  • Designed to support:
    • Higher rack densities
    • Improved power efficiency
    • Next-generation GPU thermal envelopes
This phased approach allows FarmGPU to scale efficiently today while preparing for the thermal and power demands of future AI hardware.