Choosing a Cluster

Selecting the Appropriate Tensor One Cluster

Making the right Cluster configuration choice is essential to maximising your Tensor One deployment's effectiveness and performance. The type of GPU, RAM, VRAM capacity, vCPU count, and storage options (permanent and temporary) are important factors.

To help you select an appropriate Cluster configuration, the following general guidelines are provided. Always base your decision on the demands and specifications of your particular project.

Summary

It's critical to comprehend the requirements of your model. Detailed specifications are usually available in:

• descriptions of model cards on websites such as Hugging Face.
• The model's config.json file

Useful tools to help assess your model's specific resource needs include:

• Hugging Face's Model Memory Usage Calculator
• Vokturz’s "Can It Run LLM" Calculator
• Alexander Smirnov’s VRAM Estimator

These tools provide valuable insights into resource requirements for efficient planning.

Key Selection Factors

When selecting a Cluster, focus primarily on:

• GPU
• VRAM
• Storage (Disk Size)

Each factor significantly influences your deployment's performance and efficiency.

GPU

The GPU directly impacts your project's computational capabilities, particularly for graphics-intensive tasks and machine learning applications.

Importance: GPUs are crucial for processing complex algorithms efficiently. Stronger GPUs accelerate computational tasks, enabling more advanced and demanding applications.

Selection Criteria:

• Task Requirements: Consider the computational complexity and GPU intensity of your workload.
• Compatibility: Verify GPU compatibility with your intended software frameworks.
• Energy Efficiency: Evaluate GPU power consumption, especially important for extended or continuous deployments.

VRAM (Video RAM)

VRAM is dedicated memory for the GPU, essential for tasks involving substantial graphical processing and rendering.

Importance: High VRAM capacity supports more complex graphics, larger datasets, and enhanced multitasking performance, critical for advanced graphics and AI applications.

Selection Criteria:

• Graphics Intensity: Intensive tasks (e.g., 3D rendering, advanced gaming, AI model training) require more VRAM.
• Parallel Processing: Tasks involving simultaneous data streams benefit significantly from increased VRAM.
• Future-Proofing: Opting for additional VRAM enhances adaptability to evolving project requirements.

Storage (Disk Size)

Appropriate storage solutions, both temporary and persistent, ensure optimal data management and system performance.

Importance: Adequate disk storage ensures efficient data handling, effective caching, and supports smooth project operations.

Selection Criteria:

• Data Volume: Estimate anticipated data processing and storage needs.
• Speed Requirements: Faster disk access enhances overall system responsiveness.
• Data Retention: Balance temporary (volatile) and persistent (non-volatile) storage based on your project's retention policies and operational requirements.