An open AI
community
for all
We are a community of practitioners building an open-source,
composable AI ecosystem. Our goal is to reduce the complexity of
the AI industry. Join our thriving community and share, collaborate
and innovate with us.
An open AI
community
for all
We are a community of practitioners building an open-source, composable AI ecosystem. Our goal is to reduce the complexity of the AI industry. Join our thriving community and share, collaborate and innovate with us.
Find
AiFoundry.org
Projects
# llamagator
# project-prompt
# open-llms
Out more
Welcome Susan!
Glad to join this community!
Mara joined the party
Everyone welcome Nina
Glad you're here, Tuana!
Find
out
more
AiFoundry.org
Projects
# llamagator
# project-prompt
# open-llms
Welcome Susan!
Glad to join this community!
Mara joined the party
Everyone welcome Nina
Glad you're here, Tuana!
View and join our
open-source projects
AI Foundry open source projects are Apache V2 licensed. View the code on
our GitHub, and collaborate with project contributors in our Discord Server.
Llamagator
Llamagator (your LLM aggregator) is a multi-LLM prompt testing tool.
- Test prompts against multiple LLMs or LLM versions
- Observe the relative performance of generated responses
- Run prompts multiple times to observe the reliability
- Supports local and API access to LLMs
- Licensed under Apache License, Version 2.0
View repo
$blame.ai
$blame.ai is a demo app for testing AI infrastructure from LLMs to inference engines.
We're building an open data set, application code, and reference implementation that any developer can use to showcase their AI infrastructure.
View repo
Quantization-Aware Training
A community research project to investigate data-free quantization-aware training.
View repo
View and join our
open-source
projects
AI Foundry open source projects are Apache
V2 licensed. View the code on our GitHub, and
collaborate with project contributors in our
Discord Server.
Llamagator
Llamagator (your LLM aggregator) is a multi-LLM prompt testing tool.
- Test prompts against multiple LLMs or LLM versions
- Observe the relative performance of generated responses
- Run prompts multiple times to observe the reliability
- Supports local and API access to LLMs
- Licensed under Apache License, Version 2.0
$blame.ai
$blame.ai is a demo app for testing AI infrastructure from LLMs to inference engines.
We're building an open data set, application code, and reference implementation that any developer can use to showcase their AI infrastructure.
Quantization-Aware Training
A community research project to investigate data-free quantization-aware training.
What's happening
in the community
Upcoming podcasts, virtual and physical community events for AI Foundry.
Past
Upcoming
Compiling
FOSDEM fringe:
AI plumbers conference
By Tanya Dadasheva & Roman Shaposhnik
Feb 3, 2025 Monday
9:00 AM - 5:00 PM GMT+1
Gent, Belgium
What's happening
in the community
Upcoming podcasts, virtual and physical
community events for AI Foundry.
Past
Upcoming
Compiling
FOSDEM fringe:
AI plumbers conference
By Tanya Dadasheva & Roman Shaposhnik
Feb 3, 2025 Monday
9:00 AM - 5:00 PM GMT+1
Gent, Belgium
Explore Our Latest Insights
Stay updated with our community-driven content. Read about past events and access to summaries and video replays of prior AI Hack Labs and Podcasts.
Explore Our Latest
Insights
Stay updated with our community-driven content.
Read about past events and access to summaries
and video replays of prior AI Hack Labs and
Podcasts.
Vision
& Mission
Our long-term vision is to help the industry evolve to a structure where model training and deployment for any company are open, transparent, and concise.
We desire to see the industry aligned on the principles of how those systems are structured at a high level from which every company can deploy its own engine or system, configured the way each specific company needs it. This approach will lower risks and improve AI results for all companies and practitioners. Therefore, we foster, build, and support projects that can become the engineering foundation of such training and deployment systems. We support practitioners willing to benefit from AI in their work and life, specifically models and their fine-tuning and deployment.
We are a community of practitioners building an open-source composable AI ecosystem. By collaborating on standards for everyone, we aim to reduce the complexity of the AI industry.
Our core values
Openness
We don't just mean you can use our tools, we mean you are free to use it however you want.
Integrity
Honesty, ethics, and follow-through foster trust within the community.
Empathy
Our community members are mindful and curious about other people’s motivations and ideas and help each other with compassion and kindness.
Cooperation
Our community seeks win-win solutions with mutually beneficial outcomes.
Vision
& Mission
Our long-term vision is to help the industry evolve to a structure where model training and deployment for any company are open, transparent, and concise.
We desire to see the industry aligned on the principles of how those systems are structured at
a high level from which every company can
deploy its own engine or system, configured the
way each specific company needs it. This approach will lower risks and improve AI results for all companies and practitioners. Therefore, we foster, build, and support projects that can
become the engineering foundation of such training and deployment systems. We support practitioners willing to benefit from AI in their work and life, specifically models and their fine-tuning and deployment.
We are a community of practitioners building an open-source composable AI ecosystem. By collaborating on standards for everyone, we aim to reduce the complexity of the AI industry.
Our core values
Openness
We don't just mean you can use our tools, we mean you are free to use it however you want.
Integrity
Honesty, ethics, and follow-through foster trust within the community.
Empathy
Our community members are mindful and curious about other people’s motivations and ideas and help each other with compassion and kindness.
Cooperation
Our community seeks win-win solutions with mutually beneficial outcomes.
Joining AI Foundry was a crucial decision for Cybergizer. In a post-foundational model era, owning your inference stack isn’t optional!
Sergey Sergyenko
CEO at Cybergizer
Joining AI Foundry was a crucial decision for Cybergizer. In a post-foundational model era, owning your inference stack isn’t optional!
Sergey Sergyenko
CEO at Cybergizer
Join the ai
revolution
today
revolution
today
Join the ai
revolution
today
revolution
today
Faqs
Find answers to your questions about AI Foundry
and our community-driven projects.
It may seem like that at first (after all we've got efforts ranging from AI-inference chip design to Quantization Aware Training [QAT] research) but the overarching theme for us is building an inference-focused, economically viable, decentralized, self-governed and developer-centric "Open Source AI" platform. Just like the original Cloud Foundry (from which the inspiration of our name came from) had to tackle a lot of layers at once in order to simplify the cloud application development lifecycle - we have to do the same heavy lift to allow AI practitioners to focus on code rather than infrastructure. At least Cloud Foundry didn't have to design its own alternative to Intel and AMD CPUs!
As great Alan Kay once said: "People who are really serious about software should make their own hardware". The problem is that all the successful AI acceleration chips on the market today came from the industry's long focus on training (with inference being more of an afterthought). It is now clear that at least in the enterprise, inference use cases will completely dwarf the training ones. Not only do we need an ML framework that reflects that shift we need it to co-evolve with a chip (same way that CUDA/Pytorch co-evolved with NVidia and tensorflow co-evolved with the TPU). In fact, the combination of the two is so essential and downright critical for the future of business and society that we feel that Open Source is the only way to design the two together.
Truth be told, the right way to look at the AI market is before and after the "DeepSeek moment". Not that it wasn't clear to some of us even before, but after the DeepSeek our whole industry got catalyzed around importance of inference and all the efforts (down to the custom chips) that would allow enterprises to have the lowest TCO numbers possible. C[A]IOs started demanding solutions for "OpenAI-like inference APIs around open-weight models at DeepSeek-like TCO". An Open Source platform for something like this has to be built and the time is NOW!
AI Plumbers was envisioned to be an interactive conference for all the people building low levels of the AI stack (from hardware to inference engines) - sort of like Linux people have Linux Plumbers Conference. From that standpoint, AI Plumbers is similar to an unconferences since the agenda is fluid and determined by the participants. However, an AI Plumbers involves hands-on work (don't forget to bring your laptop). We partner with other open source and AI conferneces all over the world to make it easier on travel and scheduling for participants, so keep an eye on our events section and don't miss the next one
Faqs
Find answers to your questions about AI Foundry
and our community-driven projects.
It may seem like that at first (after all we've got efforts ranging from AI-inference chip design to Quantization Aware Training [QAT] research) but the overarching theme for us is building an inference-focused, economically viable, decentralized, self-governed and developer-centric "Open Source AI" platform. Just like the original Cloud Foundry (from which the inspiration of our name came from) had to tackle a lot of layers at once in order to simplify the cloud application development lifecycle - we have to do the same heavy lift to allow AI practitioners to focus on code rather than infrastructure. At least Cloud Foundry didn't have to design its own alternative to Intel and AMD CPUs!
As great Alan Kay once said: "People who are really serious about software should make their own hardware". The problem is that all the successful AI acceleration chips on the market today came from the industry's long focus on training (with inference being more of an afterthought). It is now clear that at least in the enterprise, inference use cases will completely dwarf the training ones. Not only do we need an ML framework that reflects that shift we need it to co-evolve with a chip (same way that CUDA/Pytorch co-evolved with NVidia and tensorflow co-evolved with the TPU). In fact, the combination of the two is so essential and downright critical for the future of business and society that we feel that Open Source is the only way to design the two together.
Truth be told, the right way to look at the AI market is before and after the "DeepSeek moment". Not that it wasn't clear to some of us even before, but after the DeepSeek our whole industry got catalyzed around importance of inference and all the efforts (down to the custom chips) that would allow enterprises to have the lowest TCO numbers possible. C[A]IOs started demanding solutions for "OpenAI-like inference APIs around open-weight models at DeepSeek-like TCO". An Open Source platform for something like this has to be built and the time is NOW!
AI Plumbers was envisioned to be an interactive conference for all the people building low levels of the AI stack (from hardware to inference engines) - sort of like Linux people have Linux Plumbers Conference. From that standpoint, AI Plumbers is similar to an unconferences since the agenda is fluid and determined by the participants. However, an AI Plumbers involves hands-on work (don't forget to bring your laptop). We partner with other open source and AI conferneces all over the world to make it easier on travel and scheduling for participants, so keep an eye on our events section and don't miss the next one