--- title: 5 Techniques to Optimize LLMs in Production | daily.dev description: LLM token generation is fundamentally a memory bandwidth problem, not a compute problem — upgrading GPU compute alone won't fix slow inference. Five key... canonical: https://daily.dev/posts/5-techniques-to-optimize-llms-in-production-lmz4pypvm twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: 5 Techniques to Optimize LLMs in Production | daily.dev og:description: LLM token generation is fundamentally a memory bandwidth problem, not a compute problem — upgrading GPU compute alone won't fix slow inference. Five key... og:url: https://daily.dev/posts/5-techniques-to-optimize-llms-in-production-lmz4pypvm og:image: https://api.daily.dev/og/posts/Lmz4PyPVM.png og:image:alt: 5 Techniques to Optimize LLMs in Production og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # 5 Techniques to Optimize LLMs in Production **[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 6 min read · 0 upvotes · 0 comments ## Summary LLM token generation is fundamentally a memory bandwidth problem, not a compute problem — upgrading GPU compute alone won't fix slow inference. Five key production optimization techniques are explained visually: Flash Attention (tiles Q/K/V in SRAM to avoid quadratic HBM writes), Paged Attention (virtual-memory-inspired KV cache blocks reducing waste from 60-80% to under 4%), Continuous Batching (per-decode-step scheduling to keep GPUs saturated), Speculative Decoding (small draft model proposes tokens verified in one pass by the large model), and Kernel Fusion (merging LayerNorm/MatMul/activations into one kernel via TensorRT-LLM or torch.compile). The post also notes that these techniques assume one model per GPU, and introduces Superlinked's open-source inference engine (SIE) as a solution for sharing GPU memory across multiple small specialized models in production pipelines. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why doesn't upgrading from an A100 to an H100 GPU speed up LLM token generation much? LLM token generation is bottlenecked by memory bandwidth, not compute, so a faster GPU with more FLOPs does not help much. An A100 delivers about 300 trillion FP16 operations per second but only moves about 2 TB per second between HBM and its compute units. Every new token requires reading the model weights and the entire KV cache from memory, so the chip spends most of its time waiting for data rather than computing. _Engineers debugging GPU throughput mismatches can find deep-dive explainers like this through daily.dev._ ### How much GPU memory does paged attention save compared to naive KV cache allocation? Paged attention reduces KV cache memory waste to under 4%, compared to naive serving that reserves one contiguous memory chunk per request sized for the maximum possible sequence length. The vLLM paper measured that this naive approach wastes 60-80% of KV cache memory. Paged attention borrows the virtual memory concept from operating systems, using small fixed-size blocks mapped through a block table. _Teams tuning vLLM memory allocation can track serving optimization techniques like this via daily.dev._ ### Why doesn't switching to smaller specialized models reduce LLM inference costs? Switching to smaller models alone does not cut inference costs because production pipelines typically run several small specialized models (embedders, rerankers, extractors) alongside the main LLM, and each one requires a separate vLLM or TEI process that claims GPU memory upfront with no visibility into other processes. A 600M-parameter embedder can reserve a whole GPU it never fully uses, preventing models from sharing hardware efficiently. _Anyone weighing multi-model GPU sharing strategies can follow serving architecture debates on daily.dev._ ## Similar posts on daily.dev - [Why An LLM’s Memory Gets Expensive and How to Fix It](https://daily.dev/posts/why-an-llm-s-memory-gets-expensive-and-how-to-fix-it-tfmmfttjf) · ByteByteGo · 2 upvotes · 0 comments - [The Roadmap to Mastering LLM Inference Optimization](https://daily.dev/posts/the-roadmap-to-mastering-llm-inference-optimization-ytqsrfxjy) · Machine Learning Mastery · 3 upvotes · 0 comments - [The Hidden Bottlenecks in LLM Inference and How to Fix Them](https://daily.dev/posts/the-hidden-bottlenecks-in-llm-inference-and-how-to-fix-them-q1fibdwak) · DigitalOcean Community · 0 upvotes · 0 comments - [The Infrastructure Behind Making Local LLM Agents Actually Useful](https://daily.dev/posts/the-infrastructure-behind-making-local-llm-agents-actually-useful-ff4qnxir1) · Towards Data Science · 1 upvotes · 0 comments - [A Practical Deep Dive on LLM Inference and Optimization\!](https://daily.dev/posts/a-practical-deep-dive-on-llm-inference-and-optimization--nhcd29ei7) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments --- Tags: [#gpu](https://daily.dev/tags/gpu), [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/5-techniques-to-optimize-llms-in-production-lmz4pypvm) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"5 Techniques to Optimize LLMs in Production","url":"https://daily.dev/posts/5-techniques-to-optimize-llms-in-production-lmz4pypvm","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/5-techniques-to-optimize-llms-in-production-lmz4pypvm"},"datePublished":"2026-07-23T14:05:32.939Z","dateModified":"2026-09-14T09:10:37.111Z","description":"LLM token generation is fundamentally a memory bandwidth problem, not a compute problem — upgrading GPU compute alone won't fix slow inference. 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