Your LLM Isn’t Thinking — It’s an Engineer Pulling Weights at 10,000 Tokens Per Second

2026-07-13 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, long

Summary

vLLM, an LLM inference engine developed at UC Berkeley, significantly enhances AI model serving efficiency through several architectural innovations. Unlike the perception of LLMs as "thinking" entities, they are mechanical systems performing rapid matrix operations. vLLM's PagedAttention, inspired by virtual memory, reduces KV cache waste, enabling 2-4x more concurrent requests. Continuous batching ensures GPUs are never idle, leading to up to 24x higher throughput. Other features include chunked prefill for fair request processing, speculative decoding for 3-4x speedup, and prefix caching, which can reduce computation by 83% for common prompts. It also supports multi-GPU parallelism for models like Llama 405B and various quantisation formats (e.g., INT8, INT4) to fit 70B models on single A100 GPUs.

Key takeaway

For MLOps Engineers deploying open-source LLMs in production, vLLM presents a compelling solution to scale inference efficiently. Its innovations like PagedAttention and continuous batching can enable serving 10x more users and reduce cost per token by 80% compared to traditional methods. You should evaluate vLLM to optimize resource utilization, keep data on-premise for compliance, and gain full control over your serving stack, especially for models up to 405B parameters.

Key insights

Efficient LLM inference engines, particularly vLLM, are critical for scalable, cost-effective AI serving by optimizing GPU memory and compute utilization.

Principles

• Memory management (KV cache) dictates concurrent request capacity.
• Continuous GPU utilization maximizes inference throughput.
• Parallelism and quantization enable serving large models on constrained hardware.

Method

vLLM employs PagedAttention for KV cache, continuous batching for GPU scheduling, chunked prefill, speculative decoding, and prefix caching to optimize LLM inference performance.

In practice

• Deploy vLLM to increase concurrent requests by 2-4x via PagedAttention.
• Utilize continuous batching for up to 24x higher throughput.
• Implement speculative decoding for 3-4x effective speedup.

Topics

• LLM Inference
• vLLM
• PagedAttention
• GPU Optimization
• Continuous Batching
• Speculative Decoding
• Model Quantization

Best for: Director of AI/ML, AI Architect, NLP Engineer, Machine Learning Engineer, AI Engineer, MLOps Engineer

Related on AIssential

• Optimize, deploy, and benchmark an open-source LLM with vLLM — Efficient LLM inference is achieved through specialized memory management and serving systems like vLLM.
• Inference Optimization — How to Make LLMs Faster and Cheaper in Production — Optimizing LLM inference in production requires a stack of techniques to balance latency, throughput, and cost.
• lyogavin / airllm — AirLLM enables running large language models on low-VRAM GPUs by optimizing inference memory.
• The Inference Reckoning: How to Stop Burning Millions on Cloud LLM Tokens — High-volume LLM inference demands dedicated infrastructure to avoid escalating cloud token costs and gain control.
• Inference Optimization in LLMs: A Systems View — Inference optimization for LLMs is a layered systems problem, not a single technique, where memory bandwidth is the primary constraint.
• Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient — Continuous batching optimizes LLM inference by dynamically scheduling requests and packing tokens to eliminate padding and idle GPU time.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.