Microsoft's new open-source BitNet is revolutionizing the way we run Large Language Models (LLMs). It enables faster and easier execution of 1-bit LLMs than ever before. Performance Check ⚡: Cloud GPUs (A100/H100): Blazing fast, but come with a hefty price tag. Standard CPU Quantization (4-bit GGUF):Functional, but inference can be too slow for interactive applications and requires substantial RAM. (1-bit Inference):Benchmarks on x86 CPUs demonstrate impressive speedups of 2.37x to 6.17x, with a remarkable 71.9% to 82.2% energy reduction. Key Feature ✅: * BitNet can run a 100B parameter BitNet b1.58 model on a single CPU at human reading speed (5-7 tokens/second). * This remarkable feat is achieved through its optimized kernels for 1.58-bit ternary weights (-1, 0, +1). No need to commit fully to understand its potential. Clone the repository, download the official BitNet-b1.58-2B-4T GGUF model, and run the `setup_env.py` script to experience it firsthand. 👌 follow AIG for more such updates #AIG #AIgenralist #LLM #BitNet #Microsoft #OpenSource #AI

Your CPUs can't keep up. Too much data. Too much demand. GPUs are the way. Follow along for more on how GPU-accelerated compute offers answers to the biggest problems in AI and Analytics compute infrastructure.

🚨 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗷𝘂𝘀𝘁 𝗳𝗶𝗿𝗲𝗱 𝗮 𝘀𝗵𝗼𝘁 𝗮𝘁 𝘁𝗵𝗲 𝗚𝗣𝗨 𝗺𝗼𝗻𝗼𝗽𝗼𝗹𝘆 🚨 In a game-changing move for efficient AI, Microsoft has open-sourced bitnet.cpp — a blazing-fast 1-bit LLM inference framework optimized for CPUs. No GPUs. No cloud dependencies. Just raw speed and efficiency on everyday hardware. 💻⚡ 𝘞𝘩𝘺 𝘥𝘰𝘦𝘴 𝘵𝘩𝘪𝘴 𝘮𝘢𝘵𝘵𝘦𝘳? → Up to 6x faster inference with 82% lower energy use → Supports 100B+ parameter models on standard x86 CPUs → Uses ternary weights (-1, 0, +1) + 8-bit activations to slash memory consumption And that’s not all. They’ve also released BitNet b1.58 2B4T, the first open-source model using just 1.58 bits per weight — with surprisingly strong benchmark performance. Minimal bits. Maximum potential. 💡 For anyone building AI systems at scale — this is huge. It opens the door to powerful, local, low-cost inference. #AI #LLM #OpenSource #EfficientAI #Microsoft #bitnet #EdgeAI #AIinference #LLMoptimization

Really interesting to see Microsoft’s move with BitNet.cpp — especially since more than 12 months ago we developed MiniRAG, built around the same philosophy: efficient, local AI with no dependency on GPUs or cloud infrastructure. MiniRAG was designed to run directly on the client’s machine, performing semantic search and lightweight inference without relying on external services. It uses optimized embeddings and a local vector store, ensuring: 🚀 Fast response times on standard hardware 🔒 Full data privacy ⚙️ Offline operation and zero cloud cost 🧠 Flexibility to integrate with various compact models Seeing initiatives like BitNet.cpp confirms that local, lightweight, and accessible AI is the right direction — and that we’ve been following that path with MiniRAG for quite some time now. #AI #RAG #OpenSource #EdgeAI #EfficientAI

Meet ‘kvcached’: A Machine Learning Library to Enable Virtualized, Elastic KV Cache for LLM Serving on Shared GPUs ================================================= kvcached is an effective approach toward GPU memory virtualization for LLM serving, not a full operating system, and that clarity matters. The library reserves virtual address space for the KV cache, then maps physical pages on demand, which enables elastic sharing across models with minimal engine changes. This aligns with evidence that cross model memory coordination is essential for multi model workloads and improves SLO attainment and cost under real traces. Overall, kvcached advances GPU memory coordination for LLM serving, production value depends on per cluster validation. What kvcached changes? ================== With kvcached, an engine creates a KV cache pool that is contiguous in the virtual address space. As tokens arrive, the library maps physical GPU pages lazily at a fine granularity using CUDA virtual memory APIs. When requests complete or models go idle, pages unmap and return to a shared pool, which other colocated models can immediately reuse. This preserves simple pointer arithmetic in kernels, and removes the need for per engine user level paging. #aritificialintelligence #gpu #llm

Microsoft Releases bitnet.cpp — Run Large AI Models on CPUs This is huge. For years, running large language models meant relying on expensive GPUs or renting cloud compute. Now, Microsoft is making it possible to run serious AI workloads directly on your CPU — no GPU required. What is BitNet.cpp? BitNet.cpp is Microsoft’s official open-source inference framework for their 1-bit large language models, including the BitNet b1.58 family. Unlike traditional 16-bit or even 8-bit models, BitNet uses ternary weights: –1, 0, +1 Combined with 8-bit activations, this design drastically reduces memory requirements and power consumption while maintaining competitive performance. In practical terms, this means: -Up to 6× faster inference -Up to 82% lower energy consumption -Ability to run 100B-parameter models on a single x86 CPU -Roughly 5–7 tokens per second — about human reading speed In short: Microsoft is making large-scale AI accessible to everyone. First Open-Source 1.58-bit Model Alongside the framework, Microsoft released BitNet b1.58 2B4T — the first fully functional open-source model that uses only 1.58 bits for weights. Despite its ultra-low precision, it performs surprisingly well on reasoning and benchmark tasks. That’s why this release is drawing attention: it’s small, fast, and effective — not a toy model. Performance benchmarks show: -On ARM CPUs — 1.37× to 5.07× faster, with 55–70% less energy use -On x86 CPUs — 2.37× to 6.17× faster, with 71–82% less energy use Imagine running a 100B-parameter model on your laptop — without burning through your power supply. That’s the level of efficiency BitNet.cpp is aiming for. Getting Started If you want to try it yourself, setup is straightforward. Clone the repo -git clone --recursive https://lnkd.in/dkJhz7YE -cd BitNet -Create a Conda environment -conda create -n bitnet-cpp python=3.9 -conda activate bitnet-cpp Install dependencies -pip install -r requirements.txt Download the model -huggingface-cli download microsoft/BitNet-b1.58-2B-4T-gguf --local-dir models/BitNet-b1.58-2B-4T ... Why This Matters For years, the AI industry has been limited by GPU bottlenecks. Want to train or run a large model? Buy NVIDIA cards or rent cloud instances. Now we’re looking at a future where: Local-first AI is realistic again Developers without GPUs can still work with advanced models Energy efficiency becomes a design principle, not an afterthought Democratized access replaces “GPU gatekeeping” This isn’t just another AI release. It’s a paradigm shift — proof that large-scale AI doesn’t have to depend on high-end GPUs. Microsoft GitHub: BitNet.cpp repository 👉 https://lnkd.in/dDry8f-V #MicrosoftAI #BitNet #AIResearch #OpenSourceAI #EdgeAI #CPUInference #MachineLearning #DeepLearning #AIInnovation #EfficientAI

#NVIDIABlackwell GPUs can tackle one of the toughest challenges in data-heavy workloads: decompression. Here are the big takeaways: ⚡ Decompression Engine (DE): #Blackwell introduces dedicated #hardware to accelerate #data decompression by offloading Snappy, LZ4, and Deflate decompression—freeing GPU SMs for compute instead of byte-shuffling. 🧑 💻 nvCOMP integration: Developers don’t need to rewrite code. With nvCOMP, apps automatically use the DE when available, while staying portable across GPU generations 📈 Blackwell advantage: By combining DE with nvCOMP, Blackwell GPUs unlock faster I/O, concurrent compute + decompression, and smoother scaling for data-intensive workloads. Learn more in our latest tech blog ➡️ https://bit.ly/4hy3DtB #GPU #Developer #HPC #AcceleratedComputing #DataCenter

Intel steps up the AI hardware game with the Crescent Island GPU—focusing on inference tasks, cost-effective LPDDR5X memory, and air-cooling for enterprise servers. This could make AI more accessible for value-driven data centers. Can this shift industry standards? [@techradar] https://cstu.io/336c48

🤯 Mind Blown! Running LLMs Locally Just Got a CPU Upgrade! 🤯🚀 Microsoft's open-sourcing of BitNet.cpp is revolutionizing how we run Large Language Models! Now, you can run powerful LLMs locally on your CPU with unprecedented efficiency. This C++ inference engine for 1-bit LLMs means: ✅ Significantly faster inference speeds compared to traditional methods. ✅ Drastically reduced energy consumption, making AI more sustainable. ✅ The ability to run large models on standard CPUs, including laptops and edge devices, without needing expensive GPUs. BitNet.cpp enables local, accessible, and efficient AI inference for everyone. This is a massive step towards democratizing access to powerful language models and fostering innovation. Link: https://lnkd.in/g4miAEf2 #AI #LLMs #BitNet #CPUInference #OpenSource #TechInnovation #MachineLearning #DeepLearning

DeepSeek v3.1 Base on Google Infra - https://lnkd.in/gi4d4NWK. This blog walks through how to deploy the LLM DeepSeek 3.1 Base model. You will use state of the art GPUs (B200) set up ✅ GKE autopilot cluster ✅ A4 VM with 8 GPU (B200) ✅ vLLM server ✅ DeepSeek v 3.1 Base ✅ Configure monitoring ✅ Hugging Face This is helpful if you have access to the A4 GPU VM family. Also you will find a link to Google Doc tutorial. ➡️ https://lnkd.in/g2tVH4pN ➡️ https://lnkd.in/gi4d4NWK By Googler Ammett