Latent AI Description
We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network.
Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI.
We help companies transform into an AI factory to make better products and services.
Latent AI Alternatives
Runpod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, Runpod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, Runpod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.
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LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents.
Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development.
Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.
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DeePhi Quantization Tool
This innovative tool is designed for quantizing convolutional neural networks (CNNs). It allows for the transformation of both weights/biases and activations from 32-bit floating-point (FP32) to 8-bit integer (INT8) format, or even other bit depths. Utilizing this tool can greatly enhance inference performance and efficiency, all while preserving accuracy levels. It is compatible with various common layer types found in neural networks, such as convolution, pooling, fully-connected layers, and batch normalization, among others. Remarkably, the quantization process does not require the network to be retrained or the use of labeled datasets; only a single batch of images is sufficient. Depending on the neural network's size, the quantization can be completed in a matter of seconds to several minutes, facilitating quick updates to the model. Furthermore, this tool is specifically optimized for collaboration with DeePhi DPU and can generate the INT8 format model files necessary for DNNC integration. By streamlining the quantization process, developers can ensure their models remain efficient and robust in various applications.
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ThirdAI
ThirdAI (pronounced /THərd ī/ Third eye) is a pioneering startup in the realm of artificial intelligence, focused on developing scalable and sustainable AI solutions. The ThirdAI accelerator specializes in creating hash-based processing algorithms for both training and inference processes within neural networks. This groundbreaking technology stems from a decade of advancements aimed at discovering efficient mathematical approaches that extend beyond traditional tensor methods in deep learning. Our innovative algorithms have proven that commodity x86 CPUs can outperform even the most powerful NVIDIA GPUs by a factor of 15 when training extensive neural networks. This revelation has challenged the widely held belief in the AI community that specialized processors, such as GPUs, are vastly superior to CPUs for neural network training. Not only does our innovation promise to enhance current AI training methods by utilizing more cost-effective CPUs, but it also has the potential to enable previously unmanageable AI training workloads on GPUs, opening up new avenues for research and application in the field.
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Company Details
Company:
Latent AI
Year Founded:
2018
Headquarters:
United States
Website:
latentai.com
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Product Details
Platforms
Web-Based
Customer Support
Online Support
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