๐๐จ๐๐๐ฆ๐๐ฉ ๐๐จ๐ซ ๐๐๐๐ฅ๐๐๐ฅ๐ ๐๐๐ ๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ - ๐๐จ๐ฏ๐ข๐ง๐ ๐๐ซ๐จ๐ฆ ๐๐ฅ๐ฅ๐๐ฆ๐ ๐ญ๐จ ๐ฏ๐๐๐ 1. ๐๐ฅ๐ฅ๐๐ฆ๐: ๐๐ก๐ ๐๐๐ ๐ข๐ง๐ง๐๐ซ-๐ ๐ซ๐ข๐๐ง๐๐ฅ๐ฒ ๐๐๐ ๐๐ฎ๐ง๐ง๐๐ซ Itโs an open-source tool designed to make running LLMs locally as easy as possible, whether youโre on a MacBook, Windows PC, or Linux server. 2. ๐ฏ๐๐๐: ๐๐ก๐ ๐๐ข๐ ๐ก-๐๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐ง๐๐๐ซ๐๐ง๐๐ ๐๐ง๐ ๐ข๐ง๐ vLLM developed by UC Berkeleyโs Sky Computing Lab, is an open-source library optimized for high-throughput LLM inference, particularly on NVIDIA GPUs. 3. ๐๐ฅ๐ฅ๐๐ฆ๐ ๐ฏ๐ฌ ๐ฏ๐๐๐ (๐๐ง๐๐ฅ๐จ๐ ๐ฒ) Ollama: Like a bicycle, easy to use, great for short trips, but not suited for highways. vLLM: Like a sports car, fast and powerful, but requires a skilled driver and a good road (GPU infrastructure). 4. ๐๐ก๐๐ง ๐ญ๐จ ๐๐ฌ๐ ๐๐ฅ๐ฅ๐๐ฆ๐ Prototyping: Testing a new chatbot or code assistant on your laptop. Privacy-Sensitive Apps: Running models in air-gapped environments (e.g., government, healthcare, or legal). Low-Volume Workloads: Small teams or personal projects with a few users. Resource-Constrained Hardware: Running on CPUs or low-end GPUs without CUDA. 5. ๐๐ก๐๐ง ๐ญ๐จ ๐๐ฌ๐ ๐ฏ๐๐๐ High-Traffic Services: Chatbots or APIs serving thousands of users simultaneously. Large Models: Deploying models like DeepSeek-Coder-V2 (236B parameters) across multiple GPUs. Production Environments: Applications requiring low latency and high throughput. Scalable Deployments: Cloud setups with multiple NVIDIA GPUs. For detailed information, refer - #llminference #llms #ollama #vllm #llmops
