Skip to main content

​
Instant Clusters — On-Demand Multi-Node AI Compute

FarmGPU offers Instant Clusters — high-performance, multi-node GPU clusters that users can provision in minutes, not months, via a self-serve, on-demand experience in partnership with RunPod. These clusters bring bare-metal performance to cloud-like flexibility, eliminating the traditional barriers of hardware procurement, rack build-out, and operational complexity. FarmGPU Blog

​
Key Characteristics

One-Click Multi-Node Deployment Users can launch powerful multi-node clusters from the RunPod dashboard with a few clicks. Instant Clusters support bring-your-own Docker containers or optimized templates for training, inference, and research workflows. Pay-Per-Use Economics Billing is per-second, not per-month, giving teams the flexibility to:
  • Run experiments without upfront commitments
  • Scale up for training runs and tear down when finished
  • Avoid minimum runtime requirements or termination fees
Complete Control Users retain full control of their cluster lifecycle — start, stop, and reconfigure as needed — without long-term lock-in or opaque pricing structures.

​
Architecture & Performance Optimizations

Instant Clusters are built on the same high-performance infrastructure that powers FarmGPU’s bare-metal offerings, including: 400 GB/s between nodes with RDMA Networking Fabric Clusters leverage an OCP-compliant 800 Gbps backend fabric for GPU-to-GPU communication, delivering low-latency, high-bandwidth interconnects optimized for distributed training and collective operations. Solidigm PCIe 5.0 NVMe Local Storage Each node includes eight PCIe 5.0 NVMe SSDs, providing up to 116 GB/s of local storage throughput to ensure that model loading and data pipelines do not bottleneck training runs. FarmGPU Blog Topology-Aware Communication Clusters are optimized for high NCCL performance — the communications library that defines training throughput for large models — achieving multi-hundreds-GB/s AllReduce performance across GPU nodes.

​
Typical Use Cases

Instant Clusters are ideal for teams that need elastic scale with the performance profile of bare-metal systems, including:
  • Distributed model training, especially with PyTorch DDP, DeepSpeed, and other frameworks
  • Large-scale fine-tuning and experimentation
  • High-performance inference workloads
  • Burst compute capacity without long-term commitments
This capability gives FarmGPU customers a flexible on-ramp to production-grade AI compute without the overhead of provisioning and managing their own cluster hardware.

​
How Instant Clusters Fit the FarmGPU Platform

Instant Clusters unify the best aspects of cloud and bare metal:
  • Cloud-like experience: Self-service deployment, per-second billing, fast iteration
  • Bare-metal performance: Optimized interconnects, local NVMe, and hardware-aware tuning
  • Integration with your software stack: Works seamlessly with FarmGPU’s control plane, scheduling, and observability tools
This makes them a natural complement to both the on-demand GPU Cloud and managed infrastructure offerings, enabling:
  • Rapid experimentation before committing to dedicated clusters
  • Burst capacity beyond long-term rentals
  • A consistent interface for both transient and persistent compute