--- title: Inside vLLM: Anatomy of a High-Throughput LLM Inference... description: A comprehensive deep-dive into vLLM's architecture, covering the full stack from the basic LLM engine core to distributed multi-GPU serving. Topics include... canonical: https://daily.dev/posts/inside-vllm-anatomy-of-a-high-throughput-llm-inference-system-2025--5fkcjpua7 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025) | daily.dev og:description: A comprehensive deep-dive into vLLM's architecture, covering the full stack from the basic LLM engine core to distributed multi-GPU serving. Topics include... og:url: https://daily.dev/posts/inside-vllm-anatomy-of-a-high-throughput-llm-inference-system-2025--5fkcjpua7 og:image: https://api.daily.dev/og/posts/5fkCJPUa7.png og:image:alt: Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025) 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. # Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025) **[Hacker News](https://daily.dev/sources/hn)** · 36 min read · 2 upvotes · 0 comments ## Summary A comprehensive deep-dive into vLLM's architecture, covering the full stack from the basic LLM engine core to distributed multi-GPU serving. Topics include paged attention and KV-cache management, continuous batching, chunked prefill, prefix caching, guided decoding via finite-state machines, speculative decoding (n-gram, EAGLE, Medusa), disaggregated prefill/decode, scaling from UniProcExecutor to MultiProcExecutor with tensor and pipeline parallelism, the async distributed serving layer with data parallelism and load balancing, and benchmarking latency vs. throughput tradeoffs. Analysis is based on vLLM commit 42172ad (V1 engine). ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How does vLLM's V1 engine implement speculative decoding without a separate draft LLM? vLLM V1 avoids running a full separate draft LLM and instead uses faster, less accurate proposal schemes: n-gram matching (reusing prior token sequences from context), EAGLE (replacing the transformer stack with a lightweight MLP while keeping embeddings and LM head), and Medusa (auxiliary linear heads predicting multiple future tokens in parallel). A rejection sampler, partly written in Triton, then accepts or rejects proposed tokens against the large model's distribution. _Developers tuning inference latency can track engine internals like this on daily.dev before picking a speculative decoding strategy._ ### How does prefix caching work internally in vLLM to avoid recomputing shared prompt prefixes? Prefix caching splits token sequences into 16-token blocks, hashes each complete block (combining the previous block's hash, current tokens, and metadata), and stores them in cached_block_hash_to_block. On a later request sharing the same prefix, find_longest_cache_hit locates matching hashes via linear search and reuses those KV-cache blocks directly, skipping recomputation of prefill tokens for that prefix. _Anyone optimizing LLM serving costs can dig into caching internals like this via daily.dev before configuring prefix caching._ ### What is the difference between tensor parallelism and pipeline parallelism when scaling an LLM inference engine across GPUs? Tensor parallelism (TP) shards a model across multiple GPUs on the same node and is generally preferred over pipeline parallelism (PP) because intranode bandwidth is significantly higher than internode bandwidth, even though PP communicates less data overall. In vLLM, when a model no longer fits on one GPU, TP is applied first within a node, then PP is added across nodes if needed, coordinated through a MultiProcExecutor with per-rank worker processes. _Engineers scaling model serving across GPUs can weigh TP versus PP tradeoffs surfaced through daily.dev._ ## Community take How the wider developer community reacted, aggregated from 7 discussions and 12 comments across hackernews (as of 2026-09-13). **TL;DR:** The community is broadly appreciative of this deep-dive into vLLM's architecture, with several commenters pointing to complementary resources like nano-vllm and tiny-vllm for those wanting a more digestible codebase. A few tangential threads touch on AI-assisted coding and production bottleneck questions. **Sentiment:** 70% positive · 20% mixed · 10% skeptical **The case for** - nano-vllm (~5k LOC) is highlighted as an excellent companion resource for understanding vLLM's core mechanics without the abstraction overhead. - The post is praised for going beyond paged attention to cover the full inference stack. **The pushback** - In production, the real bottlenecks (memory bandwidth, KV cache management, scheduler overhead) remain unclear and may differ significantly from benchmark results. - vLLM's codebase is seen by some as complex enough to resemble 'code vomit,' raising questions about long-term maintainability. **By community** - hackernews (positive): Developers appreciate the comprehensive coverage and actively share alternative minimal implementations (nano-vllm, tiny-vllm) as complementary learning resources, with a few open questions about production behavior and codebase quality. **Open questions** - In real production workloads, what is the primary bottleneck — memory bandwidth, KV cache management, or scheduler overhead — and does it differ from benchmark conditions? - How does vLLM's paged attention compare to Radix Attention used in SGLang? - Will models eventually improve enough that a system like vLLM could be generated cleanly via AI coding tools? **Highlights** > Another great way to understand how vllm works is to read the code of nano-vllm[1]. It's basically "vllm but cut down to size. It's ~5kloc, supports just one model, disposes of some of the abstraction layers that vllm needs due to its codebase size, but contains all the major pieces that make an inference engine fast. [1] https://github.com/GeeeekExplorer/nano-vllm > — [miki123211 on hackernews · 1 comments](https://news.ycombinator.com/item?id=49204082) > vLLM is originally marketed as paged attention, but in hindsight, separating the web server and GPU process, continuous batching, kv caching / chunking, and a huge model library including low precision mattered more. I wonder how much it would cost to vibe code the whole thing from scatch? I wonder how much better models need to get before such a thing wouldn't look like code vomit? > — [gdiamos on hackernews · 2 comments](https://news.ycombinator.com/item?id=49205405) > Great breakdown, thanks for writing this up. One thing I’m still unclear on: in real production workloads, what ended up being the main bottleneck first — memory bandwidth, KV cache management, or scheduler overhead? Curious how much of this showed up only under sustained load versus benchmarks. > — [bitkin\_dev on hackernews](https://news.ycombinator.com/item?id=46741313) > Love that this goes beyond paged attention. Curious how this compares with Radix Attention [1]? [1] https://sgl-project-sglang-93.mintlify.app/concepts/radix-at... > — [BinRoo on hackernews](https://news.ycombinator.com/item?id=49203643) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=49202852) · 120 points · 8 comments - [hackernews](https://news.ycombinator.com/item?id=45094003) · 4 points · 2 comments - [hackernews](https://news.ycombinator.com/item?id=45199768) · 3 points · 0 comments - [hackernews](https://news.ycombinator.com/item?id=45103764) · 3 points · 0 comments - [hackernews](https://news.ycombinator.com/item?id=46601078) · 2 points · 0 comments - [hackernews](https://news.ycombinator.com/item?id=46741285) · 1 points · 2 comments - [hackernews](https://news.ycombinator.com/item?id=45124771) · 1 points · 0 comments ## Similar posts on daily.dev - [Understanding LLM Inference Engines: Inside Nano-vLLM \(Part 1\)](https://daily.dev/posts/understanding-llm-inference-engines-inside-nano-vllm-part-1--of1uf3iim) · Hacker News · 1 upvotes · 0 comments - [Why vLLM is the best choice for AI inference today](https://daily.dev/posts/why-vllm-is-the-best-choice-for-ai-inference-today-7zxdzzpx7) · Red Hat Developer · 0 upvotes · 0 comments - [vLLM Teardown Part 2: The Virtual Memory for LLMs](https://daily.dev/posts/vllm-teardown-part-2-the-virtual-memory-for-llms-uzjhgrxwa) · Medium · 0 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 - [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 --- Tags: [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/inside-vllm-anatomy-of-a-high-throughput-llm-inference-system-2025--5fkcjpua7) ```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":"Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025)","url":"https://daily.dev/posts/inside-vllm-anatomy-of-a-high-throughput-llm-inference-system-2025--5fkcjpua7","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/inside-vllm-anatomy-of-a-high-throughput-llm-inference-system-2025--5fkcjpua7"},"datePublished":"2026-08-07T04:40:46.885Z","dateModified":"2026-09-13T21:57:47.794Z","description":"A comprehensive deep-dive into vLLM's architecture, covering the full stack from the basic LLM engine core to distributed multi-GPU serving. 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