--- title: "EP223: Ollama vs vLLM vs SGLang" url: https://daily.dev/posts/ep223-ollama-vs-vllm-vs-sglang-fkx5nhzsm source_url: https://blog.bytebytego.com/p/ep223-ollama-vs-vllm-vs-sglang type: article source: "ByteByteGo" published: 2026-08-22T15:37:07.422Z updated: 2026-08-22T15:44:44.028Z tags: ["git", "apache-kafka", "ai-inference", "ollama", "vllm"] reading_time: 6 upvotes: 3 comments: 1 language: 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. # EP223: Ollama vs vLLM vs SGLang **[ByteByteGo](https://daily.dev/sources/bytebytego)** · 6 min read · 3 upvotes · 1 comments ## Summary A newsletter roundup covers five system-design topics: how Ollama, vLLM, and SGLang differ in serving open-weight LLMs (local FIFO queuing with GGUF models, continuous batching with PagedAttention, and prefix-aware RadixAttention caching respectively); a walkthrough of how Anthropic's text watermarking scheme biases token selection using a keyed function and detects matches later; a list of the 12 most-starred agent skill repos on GitHub; a refresher on core git commands grouped by workflow stage; and a comparison of Kafka's log-based streaming model versus RabbitMQ's broker-based queuing model. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between Ollama, vLLM, and SGLang for serving LLMs? Ollama runs pre-quantized GGUF models through a FIFO request queue, making it best for local development and laptop-scale prototyping. vLLM uses continuous batching and PagedAttention for its KV cache, optimizing for high-traffic serving with thousands of concurrent requests. SGLang uses a prefix-aware scheduler with RadixAttention to reuse shared prompt prefixes, making it ideal for AI agents, multi-turn chats, and tool loops. _daily.dev surfaces comparisons like this for engineers weighing serving stacks for their LLM workloads._ ### What is RadixAttention in SGLang used for? RadixAttention is a caching mechanism in SGLang that stores shared prompt prefixes in a radix tree so overlapping requests, such as agent tool loops or multi-turn chats, can reuse cached computation instead of recomputing it. This makes SGLang especially efficient for workloads where prompts share heavy overlap, unlike single-shot requests. _Engineers picking an inference engine for agent workloads can track deep dives like this on daily.dev._ ### What is the key architectural difference between Kafka and RabbitMQ? Kafka is a distributed log where producers append messages to partitions that persist based on retention policy, letting consumers pull at their own pace, rewind, and replay events independently. RabbitMQ is a message broker where producers publish to exchanges that route to queues, pushing messages to consumers and deleting them once acknowledged, making it suited for task distribution rather than event replay. _daily.dev helps engineers weighing Kafka against RabbitMQ compare distributed system trade-offs._ ## Community discussion Top comments from developers on daily.dev. **@psymoniko** · 0 upvotes > That was my Confusion ## Similar posts on daily.dev - [Untitled](https://daily.dev/posts/untitled-o5xos4mkj) · SitePoint · 0 upvotes · 0 comments - [From Ollama to vLLM: A Migration Guide for Growing Teams](https://daily.dev/posts/from-ollama-to-vllm-a-migration-guide-for-growing-teams-nfxuk4ill) · SitePoint · 1 upvotes · 0 comments --- Tags: [#git](https://daily.dev/tags/git), [#apache-kafka](https://daily.dev/tags/apache-kafka), [#ai-inference](https://daily.dev/tags/ai-inference), [#ollama](https://daily.dev/tags/ollama), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/ep223-ollama-vs-vllm-vs-sglang-fkx5nhzsm)