> ## Documentation Index
> Fetch the complete documentation index at: https://docs.farmgpu.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Instant Clusters

> On-demand multi-node AI compute clusters with bare-metal performance

## 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](https://blog.farmgpu.com/b200-cluster/?utm_source=chatgpt.com)

### 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.

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### 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](https://blog.farmgpu.com/b200-cluster/?utm_source=chatgpt.com)

**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.

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### 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.

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### 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