Ollama vs vLLM: From Local AI Demo to Production Inference Serving

Bashiri Smith · Facebook reel · 2026-09-01 · 0:45 · 9,843 views · Open on Facebook

Topics: LLMOps, Deployment & Monitoring, LLM Fundamentals · Level: intermediate

Summary

The video explains why developers often use Ollama while companies use vLLM. Ollama lets you download a model and run it on your own machine within minutes, which works well for development or a single user. Once many users send requests at once (the example is 1,000), the hard part becomes serving the model efficiently. vLLM handles this with continuous batching, better KV-cache management and higher GPU throughput.

Key points

• Ollama: download a model, run it locally and have a working AI app in minutes. It is a good fit for development or when you are the only user.
• Production is a different problem. With about 1,000 people sending requests at the same time, the challenge moves from running the model to serving it efficiently.
• vLLM uses continuous batching, so new requests are batched together as they arrive.
• vLLM manages the model's KV cache more efficiently, which gets much more throughput out of the same GPUs.
• vLLM is built for many requests at the same time.
• You can run the same model either way. What changes is the inference infrastructure around it.
• Getting a demo working and running an AI system in production are different engineering problems.

Resources mentioned

• Ollama · tool · x.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Open-source tool for downloading and running LLMs on your own machine with minimal setup.
  Also in: Running LLMs Locally Without an Expensive Rig (Melvin Vivas on X · notes), Adding Vercel AI Gateway as a provider in AIBackends with Devin (Melvin Vivas on X · notes), Running Qwen3.8-27B Locally on an M5 Max MacBook with Inco Splash (Melvin Vivas on X · notes), AIBackends: An API Layer Between Your App and AI Models (Now with Jev) (Melvin Vivas on X · notes) and 12 more
• vLLM · repo · github.com · free · recommended by both Bashiri Smith & Melvin Vivas
  Open-source, high-throughput LLM inference and serving engine with continuous batching and efficient KV-cache management (PagedAttention).
  Also in: Serve GLM-5.2 NVFP4 with vLLM on NVIDIA Blackwell (Melvin Vivas on X · notes), Gemma 4 Gets Up to 3x Faster with MTP Drafters (Melvin Vivas on X · notes)
• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more

Try this

• Use Ollama to run a model locally and get a prototype AI app working quickly.
• When moving to many users at once, switch to a production serving engine such as vLLM.
• Join the creator's community (comment 'production' to get the link).