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 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.
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 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.
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.
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.
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
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, 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).
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.
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.”
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
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.
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.
The launch of ChatGPT in late 2022 marked a turning point for the compute industry. Within months, AI vaulted to the top of the technology landscape, pulling entire markets along with it—applications, large language models, copilots, and more. Among the
