Ollama vs. vLLM: Who should I choose for DEEPSEEK's deployment? 90% of people choose wrong! This practical guide will help you understand in seconds!
Choosing a large model deployment tool is no longer confusing. This guide will help you make the best decision.
Core content:
1. Tool positioning: Ollama is suitable for individual users, vLLM is suitable for enterprise-level applications
2. Core differences: Deployment difficulty, response speed, hardware threshold comparison
3. Pitfall avoidance guide: Ollama and vLLM usage tips and common problem solutions
1. Tool positioning: lightweight novice vs hardcore geek
Summary in one sentence :
- Ollama
: The "Swiss Army Knife" for individual users, deploys in 5 minutes, and can run large models on laptops - vLLM
: Enterprise-level "nuclear power engine", concurrent access by a team of 100 people is as stable as an old dog
For example ?
- Scenario 1
：College students use MacBook to run Llama2 to write papers → Close your eyes and choose Ollama - Scenario 2
：E-commerce company builds AI customer service system → Grit your teeth and join vLLM
2. Core differences: One table shows the key selection points
| Comparison Items | Ollama | vLLM |
|---|---|---|
| Deployment Difficulty | ||
| Response speed | 7B model: 1-3 seconds/request (3 times faster) | |
| Hardware threshold | ||
| Hidden Skills | ||
| Suitable for |
3. Pitfall Avoidance Guide: Summary of Experience
A must-read for Ollama users
Windows users should be aware of the following :
WSL2 must be enabled when installing with the Docker method! Otherwise, the model download will fail 100% of the time Reserve 20GB+ of disk space, otherwise you will get an error message and doubt your life. Quantization model accuracy loss :
The q4 quantitative version responds quickly but may speak nonsense. It is recommended to use the original version for important tasks. Long text processing tips :
Add at startup --swap-space 8GiB, 16K word paper analysis is as stable as a dogHybrid graphics cards are a big no-no! A100+V100 performance is halved High concurrency configuration :
Asynchronous logging + dynamic batch processing, throughput easily doubled You must do the authentication yourself! The default naked interface can be hacked in minutes Build a local knowledge base in 1 hour Automatically generate 100+ popular titles No asynchronous logging → API response latency spikes during high concurrency Ignore GPU model uniformity → Inference speed fluctuates by 50% - Personal/Newbie
: No-brainer Ollama, save time, effort and hair - Technology Control/Enterprise
: vLLM is really good, but be prepared to pay a lot
vLLM Advanced Skills
4. Selection strategy: copy the homework without worrying about it
3 ways to choose Ollama with your eyes closed
✅ Want to use ChatGPT but worried about data leakage✅ Laptop/old graphics card want to experience large models✅ Hate writing code, pursue out-of-the-box use
Actual test case : A self-media team uses Ollama + GTX 3060:
Two types of requirements for vLLM
✅ Need to process very long technical documents (code/papers) ✅ Enterprise-level applications with more than 1,000 visits per day
Lessons learned from pain and suffering : A startup company’s experience with vLLM:
5. Conclusion: Adults Don’t Make Choices
