# Hardware Specifications

#### Hardware Specifications

Swarm’s **Hardware Specifications** provide detailed guidance on the minimum and recommended configurations for various node types, ensuring optimal performance for specific AI workloads. Each node type is designed to address a distinct use case within the decentralized infrastructure.

***

| **Node Type**       | **Minimum Specs**              | **Recommended Specs**          | **Optimal Use Case**                                                              |
| ------------------- | ------------------------------ | ------------------------------ | --------------------------------------------------------------------------------- |
| **AI Training**     | 8x NVIDIA A100 GPUs, 512GB RAM | 16x NVIDIA A100 GPUs, 1TB RAM  | Large-scale **model training**, distributed deep learning workloads.              |
| **Inference**       | 4x NVIDIA T4 GPUs, 128GB RAM   | 8x NVIDIA A10 GPUs, 256GB RAM  | High-throughput **model serving** for real-time predictions.                      |
| **General Compute** | 32 CPU cores, 128GB RAM        | 64 CPU cores, 256GB RAM        | **Data processing**, orchestration, and lightweight AI workloads.                 |
| **Storage**         | 2TB NVMe SSD, 10Gbps network   | 10TB NVMe SSD, 100Gbps network | **Data storage** for training datasets, model checkpoints, and archival purposes. |

***

**Key Features**

* **Tailored Configurations**:
  * Each node type is optimized for a specific workload, ensuring efficient resource utilization.
* **Scalability**:
  * Nodes can be scaled horizontally (adding more nodes) or vertically (upgrading specs) to meet workload demands.
* **High Performance**:
  * High-end configurations provide the compute, memory, and storage required for intensive AI tasks.
* **Low Latency**:
  * Storage nodes with high-speed NVMe SSDs and fast network connections enable quick access to data.

***

**Benefits**

* **Efficiency**: Optimized configurations reduce operational overhead and maximize throughput for AI workloads.
* **Reliability**: High-performance hardware ensures consistency and stability under load.
* **Flexibility**: Supports a range of workloads, from lightweight inference to large-scale training.
* **Future-Proofing**: Recommended specs are designed to accommodate evolving AI models and data requirements.

These **Hardware Specifications** ensure Swarm’s infrastructure is capable of delivering exceptional performance across diverse AI applications, making it a robust and scalable platform for distributed AI workloads.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://agiledger.gitbook.io/swarmai/technical-specifications/hardware-specifications.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.