Inference Characteristics of Llama

Inference Characteristics of Llama

While LLaMA models are known for strong performance, inference costs can scale quickly depending on deployment setup. This post explores why.

The Math Behind Inference

Each token generation involves:

• A forward pass through the entire transformer stack
• KV cache operations that scale with sequence length
• Attention mechanisms that become increasingly expensive

Surprising Bottlenecks

Llama’s inference isn't just GPU-bound:

• Memory bandwidth limits token throughput
• Prefill vs. decode stage ratio becomes significant
• Context length directly increases compute cost per token

Optimization Techniques

• Use quantization or FlashAttention for improved efficiency
• Cache early layer activations for long contexts
• Choose batch sizes carefully to maximize throughput

Llama may be free, but inference is not. Understanding the bottlenecks is key to efficient deployment.