kolosal.ai

AI that executes, not just answers. On Premise.

[kolosal.ai @kolosalAI]

Wondering if your system can run a GGUF model? Use Kolosal’s Memory Calculator to estimate model size, KV cache, and total memory required before you deploy.Try now: https://t.co/FWGx1alqBA

[kolosal.ai @kolosalAI]

Enterprise inference engines aren’t built the same. Kolosal Enterprise delivers higher throughput at scale.

See the full benchmark results: https://t.co/HZ11bmqicV

[kolosal.ai @kolosalAI]

🧠 Excited to share Kolosal AutoML v0.1.2 with the #MachineLearning community!

🔥 What's new: • Modern Gradio interface replacing Streamlit • Advanced hyperparameter optimization (Bayesian, HyperX) • Real-time model comparison & performance tracking • Secure model management with encryption • Production inference server with minimal latency
Perfect for rapid prototyping, model experimentation, and deployment. Built by ML engineers, for ML engineers.
Try it: uv run python https://t.co/hNWZ4V8VrC
#MLOps #DataScience #OpenSource #AutoML #Python

[kolosal.ai @kolosalAI]

🚀 Just dropped Kolosal AutoML v0.1.2!
✨ New Gradio web UI
⚡ Lightning-fast UV package manager
🎯 Production-ready inference server
📊 Built-in sample datasets
🔐 Secure model encryption
From data upload to deployment in minutes. No-code ML has never been this powerful!
#MachineLearning #AutoML #DataScience #AI

[kolosal.ai @kolosalAI]

Level up your ML game! We've cooked up a new tutorial with easy-to-follow steps. Happy experimenting!

Dive in: https://t.co/ptsnwFZC9R

#MachineLearning #DataScienceTutorial #LearnPython #AI

[kolosal.ai @kolosalAI]

Kolosal AutoML v0.1.1 is now officially released! This version delivers significant enhancements:
🚀 Faster training & inference (improved epoch times, lower latency).
📊 Up to 3x faster HTML/Markdown report generation.
🛠️ Critical fixes for training engine stability, device optimizer (enhanced GPU detection & fallback), and correct SHAP/feature-importance plot embedding.
📖 New hands-on tutorial notebook (Kolosal_AutoML_Tutorial.ipynb) now available.
Explore the update:
Main Repository: https://t.co/OwhWA4ByDo
Tutorial Notebook: https://t.co/vqSMa2wJjs

#switchtoKolosal
#runitLocalwithKolosal

[kolosal.ai @kolosalAI]

Announcing Kolosal AutoML v0.1.1: Enhanced performance with faster training, inference, and reporting. Includes key bug fixes and a new hands-on tutorial.

Learn more & download: https://t.co/qHFuBbBsVY
Tutorial: https://t.co/ptsnwFZC9R

#MLOps #AI #DeveloperTools #Update

[kolosal.ai @kolosalAI]

GPU now just getting cheaper and reachable, Intel released its Intel Arc GPU. And you know what? This is the time to start your local LLM journey.

#switchtoKolosal
#runitLocalwithKolosal

[kolosal.ai @kolosalAI]

@ionleu Local LLM platform but its open source, faster, and lighter

[kolosal.ai @kolosalAI]

@patro_satyam Pricing is relative to the customer, so if you could make the pricing makes sense to your target market, no matter the number you give, there is no such a thing of low prices. But generally, pricing low is not building trust, trust is built by your actual product.

[kolosal.ai @kolosalAI]

We should've aware that AI is not always telling the truth about anything, they are tasked and train to follow your instruction, not giving a philosophically true fact.

[kolosal.ai @kolosalAI]

Asking about https://t.co/2BxL5VTJsd in Kolosal AI using gemma 4B, and i quote:

"Key Features and What Makes Kolosal Different:

Designed for Low-End Hardware: Unlike many other local LLMs, Kolosal was built from the ground up to run efficiently on
CPUs with relatively modest RAM (around 8GB – 16GB is recommended, though it can run
on less). This is a huge advantage, opening up local LLM use to a much wider range of users.
Quantized Model: Kolosal uses a technique called "quantization" to drastically reduce the size of the model. This means it uses
fewer bits to represent the numbers within the model, significantly decreasing its memory footprint and
improving speed.
Excellent Performance: Despite its relatively small size (around 13 billion parameters), Kolosal performs remarkably well on
many tasks, often outperforming larger models on similar hardware. It’s particularly strong in creative
writing and generating code.
Ease of Use: Kolosal is designed to be very user-friendly. It’s available as a downloadable application (available for Windows,
macOS, and Linux) and can be run directly from your file system.
Open Source: The entire model and its codebase are open source, allowing for community contributions and further
development."

try it on kolosal AI

[kolosal.ai @kolosalAI]

@PLBompard deploy local LLM simple and fast, check out on https://t.co/lbY2w5G8g9

[kolosal.ai @kolosalAI]

@code_kartik True, even if AI exist, you still have to learn the basics so that those LLMs won't lead you to doom

[kolosal.ai @kolosalAI]

@ionleu Deploy and run your AI locally

https://t.co/lbY2w5FAqB

[kolosal.ai @kolosalAI]

Is ML hard? Yes. Thats why Kolosal make it simple, Kolosal AutoML makes your model building journey simple, fast, and easy.

Check out on https://t.co/qHFuBbAV6q

#switchtoKolosal
#runitLocalwithKolosal

[kolosal.ai @kolosalAI]

Ready to Launch Kolosal Plane, stay tuned for the update!

Kolosal Plane is a platform to finetune and train your local LLMs as your own whish, with your own data. We build this to enchance the performance of small language models to your specific task.

#switchtoKolosal
#runitLocalwithKolosal

[kolosal.ai @kolosalAI]

@EastonKimm It is a app, easy, simple, and fast to deploy your own LLM

[kolosal.ai @kolosalAI]

People think ML is complicated, we make it simple. Just prepare your data, and boom, a report generated in your local environment.
Check out on: https://t.co/IIJaglXlJS

#switchtoKolosal
#runitLocalwithKolosal