"8 synced machines without Docker, Kubernetes, or CUDA requirements, just InfiNET running Bare Metal." - Ean Mikale, JD, Principal Engineer, Infinite 8 Industries, Inc. | Heterogeneous 8 GPU/CPU Cluster (Arm64/x86) | InfiNET Ogdoad | Wave Computation https://lnkd.in/guPC-yZr

🚀 Why let LLMs have all the fun? It's time to run our vector databases like a LLM! An exciting change is coming in TxtAI 9.1. Vector databases fully on the GPU, just like a LLM! All vectors are persisted as Safetensors files, loaded onto the GPU with FP4/NF4/INT8 quantization support and efficient on-GPU matrix multiplication. With new methods such as REFRAG, perhaps we're heading towards Vectors, RAG and LLMs merging into a single structure? We'll see! Link: https://lnkd.in/eQgUUmBp

Building a Lightning-Fast LLM from Scratch? Here’s How It’s Really Done! If you want to deeply understand the difference between “just running a model” and “squeezing every ounce of performance with your bare hands,” Andrew Chan’s deep-dive blog is your blueprint. It’s also brutally honest about where compiler or hardware quirks can destroy your wins, and how hands-on profiling plus a willingness to rewrite simple operations for performance can make or break your project. Andrew covers: - Building LLM inference architecture: attention, feedforward, custom transformer blocks, and KV cache use. - Hardware bottlenecks, focusing on memory bandwidth and the benefits of quantization. - Optimization steps: OpenMP threading, CUDA matrix multiplication, kernel fusion, memory tricks, and loop unrolling for maximum speed. - Benchmark: Achieves 63 tok/s on a RTX 4090, surpassing projects like llama.cpp.

🧮 What does 8×7B actually mean? It is NOT 8 experts with 7B active parameters per token. Turns out it’s actually 13B active parameters. But wait — where does 13B come from? In the world of Mixture of Experts (#MoE), even the simplest questions get surprisingly complex: ❓ How much storage do you actually need? ❓ How much compute does that translate to? ❓ What are the real bottlenecks — memory, compute, or communication? ⁉️ And how does Cerebras solve GPU bottlenecks? If you’ve ever tried to make sense of MoE math, our next post in the MoE 101 guide by Daria Soboleva (and interactive calculator) breaks it all down. https://lnkd.in/g7N6dh69

❓Still confused between LoRA and QLoRA? You’re not alone. Fine-tuning LLMs isn’t about retraining the whole model anymore — it’s about smarter, leaner methods. Here’s the quick breakdown 🧩: ✅ LoRA → Lightweight low-rank fine-tuning ✅ QLoRA → LoRA + 4-bit quantization (optimized for memory) ✅ Key differences → Depends on your hardware + budget ✅ Plus → Visual math + real-world use cases 💡 The right choice can save you hours of compute and $$$ on GPUs. 👉 Swipe through this guide to know which one fits your next project. Kudos to Naresh Edagotti for curating this.

A computer code to maintain the structural rules of materials The properties of a material are determined by its structure, and quantum materials are no exception. Some atomic structures are more likely to give rise to exotic quantum properties than others. For example, square lattices can serve as a platform for high-temperature superconductors, while other shapes known as Kagome and Lieb lattices can support the creation of materials that could be useful for quantum computing. #Let's grow together#SGTOOLSNC#www.sgtool.it#

Is this the architecture for **next-generation vector databases**? Indices that respect the manifold structure of data, not just its geometric projection. → Full DeepEncoder architecture (SAM + CLIP + projector) in Rust with cross-platform GPU support (WGPU/CUDA) → Five resolution modes: 64 to 400 tokens with configurable compression ratios → ArrowSpace v0.18.0 with build_energy and search_energy—no cosine similarity in the entire pipeline https://lnkd.in/e5haSPvH #vectorDB #DeepSeek #compression #texttoimage #diffusion #dispersion

Left-to-right is a clock, then an asynchronous clock (push-to-pulse), then another debounced button for data input. At the end is a shift register. This model can be used to provide a tangible demonstration of CPU architecture theories, such as clock signals, and registers, as well providing a way to practice binary and binary numbers.