AI Infrastructure

Agentic AI AI Infrastructure

IBM Released new Granite 4.0 Models with a Novel Hybrid Mamba-2/Transformer Architecture: Drastically Reducing Memory Use without Sacrificing Performance
ByRicardo October 2, 2025

IBM simply launched Granite 4.0, an open-source LLM household that swaps monolithic Transformers for a hybrid Mamba-2/Transformer stack to chop serving reminiscence whereas maintaining high quality. Sizes span a 3B dense “Micro,” a 3B hybrid “H-Micro,” a 7B hybrid MoE “H-Tiny” (~1B lively), and a 32B hybrid MoE “H-Small” (~9B lively). The fashions are Apache-2.0,…

Read More IBM Released new Granite 4.0 Models with a Novel Hybrid Mamba-2/Transformer Architecture: Drastically Reducing Memory Use without Sacrificing Performance
AI Infrastructure Artificial Intelligence

MLPerf Inference v5.1 (2025): Results Explained for GPUs, CPUs, and AI Accelerators
ByRicardo October 1, 2025October 1, 2025

What MLPerf Inference Actually Measures? MLPerf Inference quantifies how briskly a whole system ({hardware} + runtime + serving stack) executes fastened, pre-trained fashions underneath strict latency and accuracy constraints. Results are reported for the Datacenter and Edge suites with standardized request patterns (“eventualities”) generated by LoadGen, guaranteeing architectural neutrality and reproducibility. The Closed division fixes…

Read More MLPerf Inference v5.1 (2025): Results Explained for GPUs, CPUs, and AI Accelerators
AI Infrastructure AI Paper Summary

IBM and ETH Zürich Researchers Unveil Analog Foundation Models to Tackle Noise in In-Memory AI Hardware
ByRicardo September 21, 2025

IBM researchers, along with ETH Zürich, have unveiled a brand new class of Analog Foundation Models (AFMs) designed to bridge the hole between giant language fashions (LLMs) and Analog In-Memory Computing (AIMC) {hardware}. AIMC has lengthy promised a radical leap in effectivity—operating fashions with a billion parameters in a footprint sufficiently small for embedded or…

Read More IBM and ETH Zürich Researchers Unveil Analog Foundation Models to Tackle Noise in In-Memory AI Hardware
AI Infrastructure Artificial Intelligence

MIT’s LEGO: A Compiler for AI Chips that Auto-Generates Fast, Efficient Spatial Accelerators
ByRicardo September 19, 2025

Table of contents Hardware Generation without Templates Input IR: Affine, Relation-Centric Semantics (Deconstruct) Front End: FU Graph + Memory Co-Design (Architect) Back End: Compile & Optimize to RTL (Compile & Optimize) Outcome Importance for each segment How the “Compiler for AI Chips” Works—Step-by-Step ? Where It Lands in the Ecosystem? Summary MIT researchers (Han Lab)…

Read More MIT’s LEGO: A Compiler for AI Chips that Auto-Generates Fast, Efficient Spatial Accelerators
AI Infrastructure AI Shorts

MoonshotAI Released Checkpoint-Engine: A Simple Middleware to Update Model Weights in LLM Inference Engines, Effective for Reinforcement Learning
ByRicardo September 16, 2025

MoonshotAI has open-sourced checkpoint-engine, a light-weight middleware aimed toward fixing one of many key bottlenecks in giant language mannequin (LLM) deployment: quickly updating mannequin weights throughout hundreds of GPUs with out disrupting inference. The library is especially designed for reinforcement studying (RL) and reinforcement studying with human suggestions (RLHF), the place fashions are up to…

Read More MoonshotAI Released Checkpoint-Engine: A Simple Middleware to Update Model Weights in LLM Inference Engines, Effective for Reinforcement Learning
AI Infrastructure AI Shorts

Software Frameworks Optimized for GPUs in AI: CUDA, ROCm, Triton, TensorRT—Compiler Paths and Performance Implications
ByRicardo September 14, 2025

Table of contents What actually determines performance on modern GPUs CUDA: nvcc/ptxas, cuDNN, CUTLASS, and CUDA Graphs ROCm: HIP/Clang toolchain, rocBLAS/MIOpen, and the 6.x series Triton: a DSL and compiler for custom kernels TensorRT (and TensorRT-LLM): builder-time graph optimization for inference Practical guidance: choosing and tuning the stack Deep-learning throughput hinges on how successfully a…

Read More Software Frameworks Optimized for GPUs in AI: CUDA, ROCm, Triton, TensorRT—Compiler Paths and Performance Implications
AI Infrastructure AI Paper Summary

ParaThinker: Scaling LLM Test-Time Compute with Native Parallel Thinking to Overcome Tunnel Vision in Sequential Reasoning
ByRicardo September 9, 2025

Why Do Sequential LLMs Hit a Bottleneck? Test-time compute scaling in LLMs has historically relied on extending single reasoning paths. While this strategy improves reasoning for a restricted vary, efficiency plateaus rapidly. Experiments on DeepSeek-R1-distill-Qwen-1.5B present that growing token budgets past 32K (up to 128K) yields negligible accuracy positive aspects. The bottleneck arises from early…

Read More ParaThinker: Scaling LLM Test-Time Compute with Native Parallel Thinking to Overcome Tunnel Vision in Sequential Reasoning
AI Infrastructure AI Paper Summary

How to Cut Your AI Training Bill by 80%? Oxford’s New Optimizer Delivers 7.5x Faster Training by Optimizing How a Model Learns
ByRicardo August 29, 2025August 29, 2025

Desk of contents The Hidden Cost of AI: The GPU Bill But what if you could cut your GPU bill by 87%—simply by changing the optimizer? The Flaw in How We Train Models FOP: The Terrain-Aware Navigator FOP in Practice: 7.5x Faster on ImageNet-1K Why This Matters for Business, Practice, and Research How FOP Changes…

Read More How to Cut Your AI Training Bill by 80%? Oxford’s New Optimizer Delivers 7.5x Faster Training by Optimizing How a Model Learns
AI Infrastructure Artificial Intelligence

Your LLM is 5x Slower Than It Should Be. The Reason? Pessimism—and Stanford Researchers Just Showed How to Fix It
ByRicardo August 26, 2025August 26, 2025

Desk of contents The Hidden Bottleneck in LLM Inference Amin: The Optimistic Scheduler That Learns on the Fly The Proof Is in the Performance: Near-Optimal and Robust Conclusion FAQs Within the fast-paced world of AI, massive language fashions (LLMs) like GPT-4 and Llama are powering all the pieces from chatbots to code assistants. However right…

Read More Your LLM is 5x Slower Than It Should Be. The Reason? Pessimism—and Stanford Researchers Just Showed How to Fix It
AI Infrastructure Artificial Intelligence

How Do GPUs and TPUs Differ in Training Large Transformer Models? Top GPUs and TPUs with Benchmark
ByRicardo August 25, 2025August 25, 2025

Each GPUs and TPUs play essential roles in accelerating the coaching of huge transformer fashions, however their core architectures, efficiency profiles, and ecosystem compatibility result in important variations in use case, pace, and adaptability. Structure and {Hardware} Fundamentals TPUs are customized ASICs (Software-Particular Built-in Circuits) engineered by Google, purpose-built for extremely environment friendly matrix operations…

Read More How Do GPUs and TPUs Differ in Training Large Transformer Models? Top GPUs and TPUs with Benchmark

AI Infrastructure

IBM Released new Granite 4.0 Models with a Novel Hybrid Mamba-2/Transformer Architecture: Drastically Reducing Memory Use without Sacrificing Performance

MLPerf Inference v5.1 (2025): Results Explained for GPUs, CPUs, and AI Accelerators

IBM and ETH Zürich Researchers Unveil Analog Foundation Models to Tackle Noise in In-Memory AI Hardware

MIT’s LEGO: A Compiler for AI Chips that Auto-Generates Fast, Efficient Spatial Accelerators

MoonshotAI Released Checkpoint-Engine: A Simple Middleware to Update Model Weights in LLM Inference Engines, Effective for Reinforcement Learning

Software Frameworks Optimized for GPUs in AI: CUDA, ROCm, Triton, TensorRT—Compiler Paths and Performance Implications

ParaThinker: Scaling LLM Test-Time Compute with Native Parallel Thinking to Overcome Tunnel Vision in Sequential Reasoning

How to Cut Your AI Training Bill by 80%? Oxford’s New Optimizer Delivers 7.5x Faster Training by Optimizing How a Model Learns

Your LLM is 5x Slower Than It Should Be. The Reason? Pessimism—and Stanford Researchers Just Showed How to Fix It

How Do GPUs and TPUs Differ in Training Large Transformer Models? Top GPUs and TPUs with Benchmark

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