inference
inference on Beyond Market Intelligence: a running collection of 36 stories we have gathered and hand-picked because they are worth your time. Every post here touches on inference in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around inference, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
How to pick an AI model in 2026
Navigating the AI model landscape in 2026 will demand a strategic approach. Choosing the right model requires prioritizing specific task performance, cost-effectiveness, and integration capabilities. Expect a market saturated with specialized models, making broad, general-purpose options less appealing. Focus on evaluating models based on rigorous benchmarks and real-world application testing. Consider scalability and ongoing maintenance costs as critical factors. For deeper insights into optimizing infrastructure alongside AI investment, explore our article, "Uber’s Zero Growth Stack."
Netflix Details Its In-House LLM Serving Platform with Triton and vLLM
Netflix has detailed its sophisticated in-house platform for Large Language Model (LLM) inference, leveraging Triton and vLLM to address the complexities of scaling AI. The platform’s design reflects key production lessons learned, specifically managing diverse model sizes, hardware demands, and the accelerated evolution of inference engines. This architecture allows Netflix to rapidly deploy and optimize LLMs internally. For a deeper understanding of adapting to AI’s rapid pace of change, explore our related article, "An Evolutionary Architecture Pattern for Managing AI’s Pace of Change."
The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs
Enterprises are rapidly accelerating investment in AI infrastructure, yet a significant "compute gap" exists – heavy spending outpacing the ability to truly understand and control its economics. New VentureBeat Pulse Research, surveying 107 organizations, reveals that while only 21% run AI at scale, nearly half intend to evaluate specialized AI clouds within the year, often lacking clear visibility into GPU utilization (83% below 50%) and compute costs.
AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors
Etched, a nascent AI chip startup founded by Harvard dropouts, is rapidly gaining traction, achieving a remarkable $10.3 billion valuation from prominent investors. Unlike traditional approaches reliant on GPUs, Etched's innovative chips and memory components accelerate AI model inference directly, streamlining workflows and unlocking new possibilities. This advancement positions Etched as a key player in the evolving AI landscape. For further insight into the broader impact of AI on various industries, explore our recent piece on how Expedia is leveraging AI to accelerate incident investigation.
How To Build Your Own LLM Runtime From Scratch
Ever wondered what it takes to build an LLM inference runtime from the ground up? This comprehensive guide details that journey, walking you through the creation of a small runtime called annotated-llm-runtime, all while running on an H100. We explore the intricacies of managing weights and CUDA graphs, highlighting three key bugs that shaped the development process. Delve into the complexities of AI infrastructure—as explored further in "OpenAI’s AI spending spree has ballooned to $750B"—and empower yourself with a deeper understanding of LLM technology.
![Tri-Net v2: Open-source implementation of our Scientific Reports paper on unified skin lesion and symptom-based monkeypox detection [R]](https://preview.redd.it/vwax5ludzheh1.png?width=140&height=79&auto=webp&s=25929233532a0110f28de21f8e7a57634c6f791b)
Tri-Net v2: Open-source implementation of our Scientific Reports paper on unified skin lesion and symptom-based monkeypox detection [R]
We’re pleased to announce the open-source release of Tri-Net v2, the fully reproducible research framework detailed in our recent *Scientific Reports* (Nature Portfolio) paper on unified monkeypox detection. This implementation prioritizes transparency and accessibility, featuring a leakage-free data pipeline, multiple CNN backbones (including ConvNeXt-Tiny), ensemble strategies, and Grad-CAM explainability. Installation is streamlined via `pip install mpox-trinet`, and comprehensive documentation is available on GitHub.
Inference startup Infinity raises $15M from Touring Capital, OpenAI and Anthropic researchers
Infinity, an AI infrastructure startup, has secured $15 million in funding, achieving a $100 million valuation. Backed by Touring Capital, Principal VC, and notably, researchers from OpenAI and Anthropic, Infinity is positioned to reshape how AI models are deployed and utilized. This investment underscores the growing demand for accessible and scalable AI infrastructure. For those seeking to optimize large language model performance, consider exploring "A Beginner’s Guide to Setting Up Claude Code for High Performance Agentic Programming," which details practical configurations.
NeurIPS reviews coming in soon! [D]
NeurIPS reviews are anticipated to appear around July 22nd at 5:30 PM AoE, based on observations across social platforms. For those who submitted to NeurIPS 2026 – whether to workshops or the main/other tracks – we'd welcome your perspectives on the upcoming reviews. This period marks a critical juncture for researchers. Explore insights into model performance; for example, our recent article on "Schema," a harness achieving 99% on ARC-3, offers a relevant case study in pushing boundaries. Share your thoughts and prepare for the assessments!
Are Current AI Memory Architectures Optimizing for the Wrong Abstraction? [D]
Are current AI memory architectures truly optimized for the future of human-AI collaboration? A recent exploration questions whether AI's persistent context—typically stored as facts and preferences—should evolve beyond simple recall. Imagine systems inferring higher-level patterns in user reasoning, like preferred explanatory frameworks, instead of just remembering interests. This shift could transform persistent context into an evolving model of user understanding. Could such sophisticated representations emerge organically, or do they demand fundamentally new architectures?
The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs
Enterprises are accelerating AI infrastructure spending, yet visibility into its economics lags significantly—a phenomenon we've termed the "compute gap." Across 107 organizations, intentions to evaluate specialized AI clouds are surging, even as existing GPUs sit at half utilization or less, and fewer than half rigorously track compute costs. This reveals a disconnect: organizations are buying more infrastructure faster than they can account for what they already own, signaling a shift away from traditional hyperscalers.
AWS Ships Claude Apps Gateway as Self-Hosted Control Plane for Claude Code and Claude Desktop
AWS now offers the Claude Apps Gateway, a self-hosted control plane streamlining access to Claude Code and Claude Desktop. This innovative solution centralizes crucial functions—identity, policy, telemetry, routing, and spend management—within a single, stateless container. Inference requests are efficiently directed to either Amazon Bedrock or the Claude Platform on AWS. This marks a significant step toward greater control and flexibility for developers. For a deeper understanding of Claude’s underlying architecture, explore our breakdown of the Claude Fable 5 system prompt.
12 Ways to Reduce LLM Latency and Inference Costs in Production
Scaling large language models (LLMs) effectively moves beyond simply adding more GPUs. It demands a rigorous focus on optimizing request efficiency. This article details 12 proven strategies to reduce LLM latency and inference costs in production environments. Ranked by impact, these methods address wasted work within each request—from caching and quantization to optimized prompting and batching. Discover practical techniques to empower your LLM deployments and maximize performance.