A.I. Builds

Example of a Multi-GPU Setup

For a high-end multi-GPU setup, consider the following:

  1. Motherboard: A motherboard with multiple PCIe slots, preferably supporting PCIe 4.0 for higher bandwidth.
  2. Power Supply Unit (PSU): A robust PSU with enough power and connectors for multiple GPUs.
  3. Cooling Solutions: Adequate cooling (both air and liquid cooling options) to manage the heat output of multiple GPUs.

Configuration Tips

  1. BIOS Settings: Ensure the BIOS is configured to support multi-GPU setups.
  2. Driver Installation: Install the latest NVIDIA drivers that support multi-GPU configurations.
  3. Framework Configuration: In your deep learning framework, configure the settings to utilize multiple GPUs (e.g., using torch.nn.DataParallel or torch.distributed in PyTorch).

Summary

By focusing on high VRAM, CUDA/Tensor cores, NVLink support, and efficient cooling, you can build a powerful multi-GPU setup capable of running large language models locally. Using high-end GPUs like the NVIDIA RTX 3090 or the A100 will provide the performance needed for demanding AI tasks.

Budget Build: Objective: Maximize performance while minimizing costs.

Components:

  • Two NVIDIA GeForce RTX 3060 12GB GPUs

Reason: The RTX 3060 provides a good balance of performance and cost. With 12GB of VRAM, it can handle moderate LLM inference tasks effectively.

  • Two NVIDIA Tesla K80 GPUs (24GB, dual-GPU card)

Reason: The Tesla K80 is an older model but still provides considerable compute power for a very low price. Each card effectively has two GPUs, giving you access to four GPUs within the two slots, maximizing the use of available PCIe slots.

Configuration

  • Slot 1: NVIDIA GeForce RTX 3060
  • Slot 2: NVIDIA GeForce RTX 3060
  • Slot 3: NVIDIA Tesla K80
  • Slot 4: NVIDIA Tesla K80

Approximate Cost: $1,500 - $2,000

Mid-Build

Objective: Achieve high performance with a reasonable budget, using more recent GPUs.

Components

  • Two NVIDIA GeForce RTX 3090 24GB GPUs

Reason: The RTX 3090 provides high performance and a large VRAM capacity suitable for handling more demanding LLM inference tasks.

  • Two NVIDIA Tesla V100 16GB GPUs

Reason: The Tesla V100 offers excellent performance for AI and machine learning tasks, making it a strong addition for inference workloads.

Configuration

  1. Slot 1: NVIDIA GeForce RTX 3090
  2. Slot 2: NVIDIA GeForce RTX 3090
  3. Slot 3: NVIDIA Tesla V100
  4. Slot 4: NVIDIA Tesla V100

Approximate Cost: $6,000 - $8,000

High-End Build

Objective: Maximize performance and efficiency regardless of cost, using the latest and most powerful GPUs.

Components

  1. Four NVIDIA A100 40GB GPUs

Reason: The A100 is one of the most powerful GPUs available for AI and machine learning tasks. With 40GB of VRAM per card, they can handle the most demanding LLM inference workloads efficiently.

Configuration

  1. Slot 1: NVIDIA A100 40GB
  2. Slot 2: NVIDIA A100 40GB
  3. Slot 3: NVIDIA A100 40GB
  4. Slot 4: NVIDIA A100 40GB

Approximate Cost: $50,000 - $60,000

Budget Build ~ Performance: Sufficient for moderate LLM inference tasks. The mixed use of RTX 3060s and Tesla K80s maximizes performance for the price but may require more effort in optimizing workload distribution.

Cost Efficiency: Excellent, providing a strong performance-to-cost ratio.

Mid-Build ~ Performance: High performance suitable for demanding LLM inference tasks. The RTX 3090s provide ample VRAM, while the Tesla V100s add specialized inference capabilities.

Cost Efficiency: Very good, offering a balance between cost and high performance.

High-End Build ~ Performance: Outstanding, capable of handling the most intensive LLM inference tasks with ease. The A100s are optimized for AI workloads, providing maximum efficiency.

Cost Efficiency: High initial cost but the best possible performance for LLM inference tasks.

Four NVIDIA GeForce RTX 3060 12GB GPUs are likely to provide better performance and Four NVIDIA GeForce RTX 3090 24GB GPUs are expected to deliver the highest performance.

  

📝 📜 ⏱️  ⬆️