Introducing JamAI Base

The AI Agent Powered Database for the Modern Enterprise

Empower Your Business to Build AI Solutions in Minutes, Not Months.

Next-gen collaborative AI platform: Chain spreadsheet cells into powerful pipelines

Experiment with prompts, models, and LLMs in real-time - no coding required

Transform enterprise data into actionable AI solutions, boosting innovation

Simplify AI integration, making advanced technology accessible to all business professionals

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Blog

Beyond GPUs: Why JamAI Base Moved Embedding Models to Intel Xeon CPUs

The journey of JamAI Base towards CPU-powered embedding models highlights a crucial shift in the AI landscape. By harnessing the power of Intel Xeon CPUs and OpenVINO, JamAI Base delivers a compelling combination of performance, efficiency, and cost-effectiveness. This approach democratizes access to powerful AI capabilities, making it easier for organizations of all sizes to leverage AI for transformative outcomes

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Recent Blog Posts

Liger Kernels Leap the CUDA Moat: A Case Study with Liger, LinkedIn's SOTA Training Kernels on AMD GPU

By EmbeddedLLM Team • 14 mins

Jan 6, 2025

Beyond GPUs: Why JamAI Base Moved Embedding Models to Intel Xeon CPUs

The journey of JamAI Base towards CPU-powered embedding models highlights a crucial shift in the AI landscape. By harnessing the power of Intel Xeon CPUs and OpenVINO, JamAI Base delivers a compelling combination of performance, efficiency, and cost-effectiveness. This approach democratizes access to powerful AI capabilities, making it easier for organizations of all sizes to leverage AI for transformative outcomes

vLLM Now Supports Running GGUF on AMD Radeon GPU

By EmbeddedLLM Team • 2 mins

Dec 1, 2024

vLLM Now Supports Running GGUF on AMD Radeon GPU

This guide shows the impact of Liger-Kernels Training Kernels on AMD MI300X. The build has been verified for ROCm 6.2.

Liger Kernels Leap the CUDA Moat: A Case Study with Liger, LinkedIn's SOTA Training Kernels on AMD GPU

By EmbeddedLLM Team • 8 mins

Nov 5, 2024

Liger Kernels Leap the CUDA Moat: A Case Study with Liger, LinkedIn's SOTA Training Kernels on AMD GPU

This guide shows the impact of Liger-Kernels Training Kernels on AMD MI300X. The build has been verified for ROCm 6.2.

See the Power of Llama 3.2 Vision on AMD MI300X

By EmbeddedLLM Team • 5 mins

Oct 28, 2024

See the Power of Llama 3.2 Vision on AMD MI300X

This blog post shows you how to run Meta's powerful Llama 3.2-90B-Vision-Instruct model on an AMD MI300X GPU using vLLM. We provide the Docker commands, code snippets, and a video demo to help you get started with image-based prompts and experience impressive performance

How to Build vLLM on MI300X from Source

By EmbeddedLLM Team • 8 mins

Oct 11, 2024

How to Build vLLM on MI300X from Source

This guide walks you through the process of building vLLM from source on AMD MI300X. The build has been verified for ROCm 6.2.

By EmbeddedLLM Team • 7 mins

Oct 27, 2023

High throughput LLM inference with vLLM and AMD: Achieving LLM inference parity with Nvidia

EmbeddedLLM has ported vLLM to ROCm 5.6, and we are excited to report that LLM inference has achieved parity with Nvidia A100 using AMD MI210.

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Frequently Asked Questions

Answers to your questions

Why using Embedded LLM platform does not require coding experience?

Using our platform is as simple as using a to-do list. We provide a highly intuitive and user-friendly interface that allows you to prototype and develop your own LLM pipeline swiftly and seamlessly. We also provide prompt templates to help you get started.

How can you ensure privacy and confidentiality of your LLM platform?

Our platform and services are bundled into our LLM appliance where you will have full control and ownership of the data and the LLM. Your data will always remain on-premise. Our appliance comes with access control function where administrators can set security measures based on organization’s policies.

How can I integrate your LLMs into my existing software infrastructure?

We offer API and SDK to interface with ELLM Appliances.

How is your Embedded LLM platform different from ChatGPT?

From our extensive experience advising clients on AI workflow automation, we found that there are 4 types of workload in a typical enterprise: (1) real-time ad-hoc, (2) real-time recurring, (3) batch ad-hoc, and (4) batch recurring. Current ChatGPT interface only addresses the realtime ad-hoc workflow. With our workflow autopilot interface you can breakdown a task into a series of steps just like a project manager and schedule the tasks to be done at a certain time or according to a schedule.

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