awesome-local-ai

IaaS or PaaS? Who owns and depreciates the logs, backups, GPUs, and the database(s)? K8s docs > Scheduling GPUs: https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus... : > Once you have installed the plugin, your cluster exposes a custom schedulable resource such as amd.com/gpu or nvidia.com/gpu. > You can consume these GPUs from your containers by requesting the custom GPU resource, the same way you request cpu or memory awesome-local-ai: Platforms / full solutions https://github.com/janhq/awesome-local-ai?platforms--full-so... But what about TPUs (Tensor Processing Units) and QPUs (Quantum Processing Units)? Quantum backends: https://github.com/tequilahub/tequila#quantum-backends Kubernetes Device Plugin examples: https://kubernetes.io/docs/concepts/extend-kubernetes/comput... Kubernetes Generic Device Plugin: https://gith

Hey Hackers! I've been diving deep into the world of open-weight LLMs, and I've noticed how overwhelming it can be to navigate the plethora of open-source inference tools available. To simplify the process, I've curated a comprehensive list of these tools in the GitHub repository: https://github.com/vince-lam/awesome-local-llms In this repository, I've scraped publicly available GitHub metrics like stars, contributors, issues, releases, and time since the last commit. This data provides valuable insights into the popularity and activity levels of each tool, helping users make informed decisions. Rather than categorizing the repositories into specific types, I've opted for a holistic approach. With the rapid pace of development in this field, manual labeling quickly becomes outdated. However, I'm open to suggestions on improving organizati

As a former Google employee working on their OSS stack, this feels a bit off brand. Here's a whole list of indie, OSS AI frameworks you can use: https://github.com/janhq/awesome-local-ai Disclaimer: I'm one of the core devs on Jan.