Efficient Inference: Making Models Faster in Production

Deploying an LLM is easy. Serving it efficiently, at high throughput and low cost, is brutally hard. In a production environment, you need specialized inference engines and optimization techniques.

1. Quantization

The most important optimization step. Standard models use 16-bit floating point weights (FP16).

• AWQ / GPTQ (4-bit): Reduces the memory footprint by 70%. It turns a model requiring 160GB of VRAM into one that fits on a single 40GB GPU, with minimal degradation in text quality.
• FP8: Becoming the standard for zero-loss inference on Hopper architecture GPUs (H100s).

2. PagedAttention & vLLM

During generation, attention mechanisms require caching the past state (KV cache). In standard pipelines, this memory fragments quickly, causing Out-Of-Memory (OOM) errors even when the GPU is only 60% full. vLLM introduced PagedAttention, which manages KV cache identically to an operating system's virtual memory (paging). It practically eliminates fragmentation, allowing you to batch up to 5x more requests simultaneously.

3. Continuous Batching

Traditional batching means waiting for all 8 requests in a batch to finish generating text before loading the next 8 requests. Continuous batching operates on an iteration-level. The millisecond one request finishes, the system immediately pulls a new request into the batch on the fly. This maximizes GPU utilization.

4. Speculative Decoding

The newest speed hack. An LLM calculates the next token one at a time. Speculative decoding uses a tiny, fast "draft" model (e.g., a 1B parameter model) to guess the next 5 tokens. The big, slow "target" model (e.g., 70B parameters) evaluates all 5 tokens instantly in parallel. If the draft model was right, you just got 5 tokens for the time cost of 1. This can lead to 2x-3x latency reductions.