LLM Inference Acceleration: Complete Guide

LLM inference is expensive and often slow. The good news: a well-understood toolkit of acceleration techniques can cut latency and cost dramatically. This guide covers every major technique, what it does, when it helps, and how much.

First, know your bottleneck#

You can't accelerate effectively without knowing what's limiting you. LLM inference has two phases:

• Prefill (processing the prompt) is compute-bound.
• Decode (generating tokens) is memory-bandwidth-bound.

Most interactive workloads are decode-dominated, which means the techniques that reduce memory traffic (quantization, KV cache optimization) tend to give the biggest wins. Throughput-oriented batch workloads benefit more from batching and parallelism.

1. Quantization — the highest-leverage win#

Quantization stores weights (and sometimes activations) in lower precision: FP8, INT8, or INT4 instead of FP16. Because decode is memory-bound, halving the bytes per weight roughly halves the memory traffic — directly speeding up token generation and freeing VRAM for larger batches or longer context.

2. Continuous batching — free throughput#

Traditional batching waits for every request in a batch to finish before starting the next. Continuous (in-flight) batching lets new requests join the running batch as soon as slots free up, dramatically improving GPU utilization under concurrent load.

This is table-stakes in modern engines (vLLM, SGLang, TensorRT-LLM all do it) — make sure it's enabled. It's one of the largest throughput wins available and costs you nothing in quality.

3. KV cache optimization#

The KV cache stores attention keys and values so the model doesn't recompute them for every token. It's essential — but it grows with sequence length and batch size and can dominate VRAM.

Key techniques:

• Paged KV cache (PagedAttention) — eliminates memory fragmentation, packing more requests into the same VRAM.
• Prefix caching — reuse the KV cache across requests sharing a prompt prefix (system prompts, few-shot examples, multi-turn chat). Huge TTFT win for repeated prefixes.
• KV cache quantization — store the cache in FP8/INT8 to fit longer contexts.

4. Speculative decoding — latency for low batches#

Speculative decoding uses a small, fast draft model to propose several tokens, which the large model then verifies in a single forward pass. When the draft is accepted, you get multiple tokens for the cost of one decode step.

It can improve effective TPS by 1.5-3× at low-to-medium batch sizes. The benefit shrinks at high batch sizes (where the large model is already well-utilized) and depends on the draft model's acceptance rate.

5. Model parallelism — when one GPU isn't enough#

When a model is too large for a single GPU, split it across several:

• Tensor parallelism (TP) — split individual layers across GPUs; lowest latency, needs fast NVLink.
• Pipeline parallelism (PP) — split layers into stages across GPUs/nodes; introduces "bubbles."
• Expert parallelism (EP) — shard MoE experts; high throughput for mixture-of-experts models.

Use TP within a node (high-bandwidth NVLink), PP across nodes as a last resort, and EP for large MoE models.

6. Disaggregation — independent scaling#

Disaggregation runs prefill and decode on separate, independently-scaled hardware — prefill on compute-optimized GPUs, decode on bandwidth-optimized ones. This matches each phase to the hardware it needs and scales them independently for cost efficiency. NVIDIA Dynamo provides dynamic disaggregation.

Putting it together#

A practical acceleration playbook for a typical deployment:

1. Quantize the model (FP8 or INT4) — biggest single win.
2. Enable continuous batching — if it isn't already.
3. Enable prefix caching — especially if you have shared system prompts.
4. Add speculative decoding — if you're latency-bound at low batch sizes.
5. Scale with parallelism / disaggregation — when you outgrow one GPU.