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Compiling LLMs into a MegaKernel: A Path to Low-Latency Inference

LLM Chat via SSH

Tokasaurus: An LLM Inference Engine for High-Throughput Workloads

What is currently the best LLM model for consumer grade hardware? Is it phi-4?

Show HN: Maestro – A Framework to Orchestrate and Ground Competing AI Models

Does Field Ordering Affect Model Performance?

LLM function calls don't scale; code orchestration is simpler, more effective

LLM-D: Kubernetes-Native Distributed Inference at Scale

llm-d, Kubernetes native distributed inference

Podcast: Platform Engineering for AI: Scaling Agents and MCP at LinkedIn

QCon AI New York Chair Wes Reisz talks with LinkedIn’s Karthik Ramgopal and Prince Valluri about enabling AI agents at enterprise scale. They discuss how platform teams orchestrate secure, multi-agentic systems, the role of MCP, the use of foreground and background agents, improving developer experience, and reducing toil. By Karthik Ramgopal, Prince Valluri

Replit Introduces New AI Integrations for Multi-Model Development

Replit has introduced Replit AI Integrations, a feature that lets users select third-party models directly inside the IDE and automatically generate the code needed to run inference. By Daniel Dominguez

Built a GGUF memory & tok/sec calculator for inference requirements – Drop in any HF GGUF URL

[D] How did Gemini 3 Pro manage to get 38.3% on Humanity's Last Exam?

How to use an LLM Gateway for Request-Level Budget Enforcement

[R] [N] TabPFN now scales to millions of rows (tabular foundation model)

average tech american compagny when you ask to release a 100 parameters ai model outdated since 2017 who counts the number of tiles in a bathroom(the model is too dangerous for the user)

[D] Published paper uses hardcoded seed and collapsed model to report fraudulent results

Is MCP overrated?

Ministral-3 has been released

Only the real ones remember (he is still the contributor with the most likes for his models)

mindsdb

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mindsdb

Federated query engine for AI - The only MCP Server you'll ever need

microsoft

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onnxruntime

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

huggingface

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pytorch-image-models

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

microsoft

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qlib

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

deepspeedai

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DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

aws

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amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Deci-AI

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super-gradients

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

modelscope

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modelscope

ModelScope: bring the notion of Model-as-a-Service to life.