Accelerating Large Language Model Inference: Techniques and Framework Recommendations

We recently deployed a dedicated inference cluster for large language models (LLMs) in production, achieving up to a 50% speedup for models as large as 70B parameters while reducing deployment costs.

The rapid growth of model size, as highlighted by OpenAI’s scaling laws and Hyung‑Won Chung’s 2023 talk, creates increasing demand for faster inference and higher throughput.

This article reviews the main challenges of LLM inference and outlines four major optimization directions: improving attention computation, managing KV‑cache memory, reducing the amount of active parameters, and leveraging tensor parallelism.

Llama 2 Model Structure – Llama 2 follows the decoder‑only Transformer architecture (CausalLM). The attention module dominates inference time, making it the primary target for acceleration.

Attention‑Computation Optimizations – FlashAttention reduces memory traffic by tiling the attention matrix and keeping intermediate results in fast SRAM, yielding 1.5×–3× end‑to‑end speedups on models such as BERT‑large and GPT‑2.

KV‑Cache Memory Management – PageAttention (used in VLLM) partitions the KV‑cache into fixed‑size pages, allowing non‑contiguous allocation and sharing, which dramatically cuts memory fragmentation and boosts throughput by more than tenfold for 13B‑parameter models.

Parameter‑Reduction via MoE – Mixture‑of‑Experts (MoE) architectures like Mixtral 8×7B activate only a small subset of parameters per token, achieving comparable or better performance than dense 70B models while using only ~13B active parameters, resulting in up to 4× faster inference.

Tensor Parallelism – By splitting weight tensors across multiple GPUs, tensor parallelism enables the deployment of models that exceed a single GPU’s memory capacity, effectively scaling inference performance.

Based on extensive internal testing, we recommend several inference frameworks that support these optimizations, including those with native FlashAttention, PageAttention, MoE, and tensor‑parallel capabilities.

In summary, combining attention‑level optimizations, efficient KV‑cache handling, MoE sparsity, and tensor parallelism can substantially improve LLM inference speed and cost‑effectiveness, and we anticipate further advances in this space.