Pros of Running LLMs Locally Cons of Running LLMs Locally Factors to Consider When Choosing a Deployment Strategy for LLMs Conclusion In recent months, we have witnessed remarkable advancements in the realm of Large Language Models (LLMs), such as ChatGPT, Bard, and LLaMA, which have ...
Today, bothchatbots have been so tightly censoredthat they won't even help you write a fictional crime novel with violent scenes. Some AI chatbots won't even talk about religion or politics. Although LLMs you can set up locally aren't entirely censorship-free, many of them will gladly do...
LLMs produce results like this using probability distributions. It works something like this, they start by looking at the text in the question and determine that the word “Cogito” has the highest probability of being the first word of the answer. From there, they look at the question and...
You can fine-tune the model for your specific needs, ensure sensitive data stays internal, and avoid dependency on external providers' updates or outages. The cons? It's expensive, requiring significant investment in infrastructure, ongoing maintenance, and expertise. Also, keeping the m...
vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache Optimized CUDA kernels vLLM is fle...
Pros of Running LLMs Locally Cons of Running LLMs Locally Factors to Consider When Choosing a Deployment Strategy for LLMs Conclusion In recent months, we have witnessed remarkable advancements in the realm of Large Language Models (LLMs), such as ChatGPT, Bard, and LLaMA, which have r...