Vast.ai

In case you missed it, our August Product Update ->

Travis from Vast AI walks us through a quick overview of how to generate images using Vast AI. Disco diffusion, stable diffusion, obbabooga tutorials and more are available in #VastAI's documentation on our website. https://lnkd.in/gddHs-Qa

TGI provides an OpenAI compatible server, which means that you can integrate it into chatbots, and other applications As companies build out their AI products, they often hit roadblocks like rate limits and cost for using these models. With TGI on Vast, you can run your own models in the form factor you need, but with much more affordable compute. As inference grows in demand with agents and complicated workflows, using Vast is great for performance and affordability where you need it the most.

Discover why developers choose #VastAI for their most demanding projects! From machine learning to data analysis, we provide the power you need to succeed.

For many people and organizations, the cost of high-end hardware is prohibitive when it comes to tasks like fine-tuning large language models (#LLMs) and other AI workloads. It may not make sense to purchase a super-powered machine like the NVIDIA A100 or H100 when a more affordable option exists and can get the job done just about the same. For instance, the NVIDIA A40 and #RTX A6000 GPUs are incredibly attractive options for the more budget-conscious user – at least when compared to such expensive higher-end machines! Not only do they balance performance and cost, but they're also far more readily available than the A100 and H100 and can scale AI projects quickly.

Meta says that Llama 3.1 405B outperforms OpenAI's GPT-4 and GPT-4o as well as Anthropic's Claude 3.5 Sonnet on a number of benchmark tests. And across a range of different tasks, it's reportedly "competitive with" its closed-source rivals. Here's how the 405B model compares to other cutting-edge LLMs across commonly used benchmarks (with Gemini not included because Meta had difficulty using Google's APIs to replicate its results):

When it comes to high-performance computing (HPC), the NVIDIA H100 is among the best of the best GPUs on the market. Its predecessor, the NVIDIA A100, is also a very impressive GPU in its own right. From enterprise to exascale, these GPUs are enabling groundbreaking advancements in everything from AI research to complex scientific simulations. Today we're taking a look at how the H100 and A100 compare with each other. Depending on your use case, one or the other might be better suited for your needs and preferences. So let's dive in!

This guide will show you how to setup Infinity Embeddings to serve an LLM on Vast. Infinity Embeddings is a helpful serving framework to serve embedding models. It is particularly great at enabling embedding, re-ranking, and classification out of the box. It supports multiple different runtime frameworks to deploy on different types of GPU’s while still achieving great speed. Infinity Embeddings also supports dynamic batching, which allows it to process requests faster under significant load. One of its best features is that you can deploy multiple models on the same GPU at the same time, which is particularly helpful as often times embedding models are much smaller than GPU RAM. We also love that it complies with the OpenAI embeddings spec, which enables developers to quickly integrate this into their application for rag, clustering, classification and re-ranking tasks.

This month, NextEpoch 2024 brought together a diverse group of college students to explore the intersection of machine learning and biological research. Held from August 5–7, 2024, the workshop covered a range of topics and included coding sessions that demonstrated the essential role of AI in RNA structure prediction. Here at Vast.ai, we're proud to be a NextEpoch sponsor, providing not only the prizes this year but also the GPUs that powered the participants' work – whether they were coding online or on-site at Rouskin Lab. This year's program, led by Harvard Medical School Assistant Professor Silvi Rouskin and co-sponsored by the Burroughs Wellcome Fund, was a resounding success! Perhaps most importantly, the three-day workshop furthered NextEpoch's goal of advancing science by bringing machine learning to the next generation of researchers.

For many people and organizations, the cost of high-end hardware is prohibitive when it comes to tasks like fine-tuning LLMs and other AI workloads. When should you go for a super-powered machine or a more affordable option?