LLM-Specific Deployment Challenges

Traditional ML deployment and LLM deployment are not identical.

LLMs can have significant:

• memory requirements
• GPU requirements
• token-processing requirements
• model-loading requirements

Large models can require substantial memory.

Large Model
    ↓
Large Memory Requirement
    ↓
Appropriate Hardware

LLM inference may depend heavily on GPU resources.

Consider:

• GPU capacity
• utilization
• batching
• concurrency

LLM workloads are strongly affected by:

Input tokens
+
Output tokens
+
Concurrent requests

Therefore, deployment planning should account for token throughput.

For models requiring specialized environments:

Container
 ├── Model
 ├── Runtime
 ├── Dependencies
 └── Serving logic
        ↓
     GPU Host

Loading a large model can be expensive.

Poor deployment:

Every request
 ↓
Load model
 ↓
Run inference
 ↓
Unload

Better approach:

Start container
 ↓
Load model
 ↓
Keep model available
 ↓
Serve multiple requests

LLM deployment requires thinking about model size, memory, GPU utilization, token capacity, and model-loading behavior, not just traditional CPU-based application deployment.