Module 1: Introduction to LLM Deployment

Welcome to the first module of our LLM deployment course! In this module, you'll learn the fundamental concepts and requirements for deploying Large Language Models on GPU infrastructure.

Understanding LLM Architecture

Large Language Models are transformer-based neural networks that can generate human-like text. Key characteristics include:

Model Size and Parameters

Modern LLMs range from millions to hundreds of billions of parameters:

• Small Models (< 1B parameters): GPT-2, DistilBERT
• Medium Models (1B - 10B parameters): GPT-3 Small, LLaMA 7B
• Large Models (10B - 100B parameters): GPT-3, LLaMA 13B/30B
• Massive Models (> 100B parameters): GPT-4, PaLM, LLaMA 65B

Transformer Architecture

LLMs use the transformer architecture with:

# Simplified transformer components
class TransformerModel:
    def __init__(self, vocab_size, d_model, nhead, num_layers):
        self.embedding = Embedding(vocab_size, d_model)
        self.transformer = Transformer(d_model, nhead, num_layers)
        self.output = Linear(d_model, vocab_size)

    def forward(self, input_ids):
        x = self.embedding(input_ids)
        x = self.transformer(x)
        return self.output(x)

GPU Requirements for Inference

Memory Requirements

The GPU memory needed depends on several factors:

Model Size Calculation:

GPU Memory ≈ Model Parameters × Precision × Overhead

Example for LLaMA 7B (FP16):
7B parameters × 2 bytes (FP16) × 1.2 (overhead) = ~16.8 GB

Recommended GPUs

Model Size	GPU Recommendation	Memory
< 1B	RTX 4090	24 GB
1B - 7B	A10G, RTX A6000	24 GB
7B - 13B	A100 (40GB)	40 GB
13B - 30B	A100 (80GB)	80 GB
> 30B	Multi-GPU A100	160+ GB

Float16.cloud Platform Overview

Float16.cloud provides serverless GPU infrastructure specifically designed for AI workloads:

Key Features

1. On-Demand GPUs: Pay only for what you use
2. Auto-Scaling: Automatically scale based on demand
3. Multiple GPU Types: Choose the right GPU for your model
4. Container Support: Deploy with your own Docker images
5. API Access: RESTful API for deployment and management

Platform Architecture

┌─────────────┐
│ Your Model  │
└─────┬───────┘
      │
      v
┌─────────────────────────────┐
│ Float16.cloud Platform      │
│ ┌─────────────────────────┐ │
│ │ Load Balancer           │ │
│ └───┬─────────────────────┘ │
│     │                        │
│     v                        │
│ ┌─────────┐  ┌─────────┐   │
│ │ GPU Pod │  │ GPU Pod │   │
│ │ A100    │  │ A100    │   │
│ └─────────┘  └─────────┘   │
└─────────────────────────────┘

Common Deployment Patterns

Pattern 1: Single Model Deployment

One model, one GPU, simple and straightforward.

from float16 import GPUDeployment

deployment = GPUDeployment(
    model="meta-llama/Llama-2-7b",
    gpu="A100",
    replicas=1
)

Pattern 2: Multi-Replica Deployment

Same model on multiple GPUs for higher throughput.

deployment = GPUDeployment(
    model="meta-llama/Llama-2-7b",
    gpu="A100",
    replicas=3,
    load_balancing="round-robin"
)

Pattern 3: Multi-Model Deployment

Different models for different use cases.

# Model A: General queries
model_a = GPUDeployment(model="llama-7b", gpu="A10G")

# Model B: Specialized tasks
model_b = GPUDeployment(model="code-llama-13b", gpu="A100")

Key Considerations

Latency vs. Throughput

• Latency: Time to generate a single response

  • Critical for chatbots and real-time applications
  • Optimize: Use smaller models, reduce batch size

• Throughput: Requests per second

  • Critical for batch processing
  • Optimize: Use batching, multiple replicas

Cost Optimization

Balance performance and cost:

1. Right-size your GPU: Don't overprovision
2. Use auto-scaling: Scale down during low traffic
3. Optimize batch size: Maximize GPU utilization
4. Consider model quantization: Reduce memory usage

Quiz: Test Your Knowledge

Before moving to the next module, make sure you understand:

1. What factors determine GPU memory requirements?
2. What's the difference between latency and throughput?
3. What are the key features of Float16.cloud platform?

Next Steps

In the next module, we'll set up your development environment and deploy your first model!

[Continue to Module 2: Setting Up Your Environment →]