Aqueduct's LLM support makes it easy for you to run open-source LLMs on any infrastructure that you use. With a single API call, you can run an LLM on a single prompt or even on a whole dataset!
The easiest way to run open source LLMs
Aqueduct's LLM support makes it easy for you to run open-source LLMs on any infrastructure that you use. With a single API call, you can run an LLM on a single prompt or even on a whole dataset!
I am really excited to see how people actually use LLMs to solve real problems. How will you use LLMs?
Hi everyone! LLMs have taken the world by storm, but using them is a pain (or a non-starter) for most people, due to concerns around data privacy, IP ownership, and cost. Open-source LLMs, like LLaMa, Dolly, and Vicuna have enabled enterprises to think about using LLMs, but they're a pain to operate. At Aqueduct, our goal has been to enable ML teams to use the best technology without the operational nightmare of running ML in the cloud, and we're super excited to share that Aqueduct now allows y
While most solutions prescribe a "rip & replace fork-lift" strategy, Aqueduct is refreshing in its philosophy of empowering and working with your existing best-in-class ML / LLM technology choices.
The next generation of AI is in all our hands; not behind superscalar moats. This launch lets us run our own LLMs, on prem or in a secure cloud. Aqueduct makes it easy, using infrastructure you already understand.
"Aqueduct simplifies the deployment of open-source LLMs, making it easier to leverage their power for natural language processing tasks."
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.
Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.