AI Startups, Sofware Stacks
& Infrastructure Insights
Latest Stories
Old Big Blue Launches Granite 4.0. Watch Out Meta
IBM Granite 4.0: The hyper-efficient, open-source LLM for business. Featuring a hybrid Mamba/Transformer architecture, it cuts memory use by 70%+ and accelerates inference. Crucially, like Llama 3, IBM provides transparency into its 22T-token training data, ensuring enterprise trust and compliance.
PEFT: How Small Adjustments Boost LLM Performance
PEFT and LoRA are revolutionizing LLM deployment. They enable companies like Baseten to fine-tune massive base models into highly performant specialists by training only tiny adapters, dramatically cutting compute, storage, and cost.
Open Source Vector Databases Overview
Open-source vector databases are reshaping AI infrastructure. From Milvus and Qdrant to Weaviate and pgvector, these systems enable lightning-fast similarity search, powering semantic search, LLM augmentation, and multimodal AI applications as data and models scale exponentially.
The Push for Standard Protocols in the Age of AI Agents
AI agents are shifting from isolated assistants to collaborative systems. Emerging protocols like Anthropic’s MCP, AutoGen, and LangChain’s Agent Protocol promise standardized communication, bridging tools and data, and potentially redefining the role of APIs in the AI era.
LiteLLM and the Rise of the Open-Source LLM Gateway
LiteLLM simplifies access to hundreds of LLMs through a single, unified API. Instead of managing multiple SDKs and endpoints, developers get cost transparency, easy routing, and streamlined deployment—making experimentation and scaling with language models faster and more efficient.
vLLM vs Triton: Competing or Complementary
Triton is the generalist server for vision and embeddings. vLLM is the LLM specialist, optimized via PagedAttention for throughput and memory. They are complementary; hybrid deployments, often with vLLM as a Triton backend, offer peak performance for mixed AI stacks.
DIY Inference Cloud vs. Hybrid Cloud: Choosing the Right AI Stack
Building an inference cloud means choosing between DIY and hybrid. DIY offers full control with GPUs, runtimes, and vector databases in colo, while hybrid offloads heavy inference to providers, balancing performance, security, scalability, and operational simplicity.
Furiosa AI Unveils New GPU Server for Inference
In a world still largely governed by NVIDIA’s GPU dominance, Furiosa AI is pushing something different: a purpose-built inference appliance designed for data centers, not massive power budgets. Their newly announced NXT RNGD Server is positioning itself as a more
Open Source Embedding Models in Hybrid AI Deployments
When organizations look at deploying LLM infrastructure for use cases like AI-powered chat, among others, three main approaches usually come up: Public cloud: outsourcing everything to external providers. Do-it-yourself: running all infrastructure in-house. Hybrid: keeping sensitive data local while offloading
Exa Labs Building the Search Engine for AI
Exa Labs, a San Francisco–based startup, is building a search engine designed for AI applications. Unlike traditional engines for humans, its platform delivers precise, structured data retrieval tailored to the needs of large language models (LLMs).
GPU Depreciation: CoreWeave vs. Nebius
Building GPU cloud infrastructure is capital-intensive. Hyperscalers and AI cloud providers invest billions in NVIDIA GPUs, often using debt or equity. How they account for hardware’s useful life directly impacts margins, cash flow, and investor perception.
The Fall of Neocloud Provider Genesis Cloud
Genesis Cloud, a Munich-based pure-play GPU-as-a-Service (GPUaaS) provider, is reportedly in liquidation. As of now, public information is limited to a Reddit post and a mention on the company’s homepage, where it is listed as Genesis Cloud GmbH i.L. (“i.L.”
OpenRouter and the Rise of AI Model Marketplaces
Founded in 2023, OpenRouter is positioning itself as a neutral access layer in the fast-expanding AI Infrastructure ecosystem. Rather than asking developers to juggle multiple APIs and contracts, the company provides a single standards-compatible interface that connects to hundreds of
Cool Startups: Baseten and the Inference Platforms
The inference platform segment is emerging in the crowded AI infrastructure market. Rather than competing on raw GPU rentals, these companies differentiate by simplifying the complex software stack needed to run, optimize, and scale models efficiently.
Building Moats in the AI Infrastructure Industry
Talk of an AI bubble is intensifying, fueled by recent high-profile agreements among Oracle, OpenAI, Nvidia, and CoreWeave. These unconventional deals recall the complex IRU swap structures once executed by Qwest and Global Crossing two decades ago.
Insights on AI Startups, Stacks, & Infrastructure.
BGP at the Edge: How FRRouting and Layer 3 Fabrics Power 100,000-GPU Clusters
The Status Report: OSS AutoML in 2025 – Which Tools Survived the Hype
The Autograd Autopsy: Why Google Killed Its Most Elegant Library to Build JAX
The Memory Siege: China’s CXMT Targets the “Big 3” DRAM Hegemony
The “Dirty DRAM Deal” – How OpenAI Just Locked Up 40% of Global RAM
Countdown to 2028: The Year AI Feature Films Challenges Hollywood and Netflix
The Great GPU Cascade: Millions of Units Meet the 18-Month Cliff
The Power Pivot: Why Google is Becoming an Energy Giant to Survive 2031
The Great Decoupling: A 2031 Roadmap for the Global AI Infrastructure War