Search: “inference”

Computational chemistry researchers have traditionally faced a frustrating trade-off when simulating molecular interactions: use fast classical force fields that sacrifice precision or rely on accurat

Together AI presented research at ICML 2026 covering advances across the full technology stack, likely including foundational model improvements, inference optimization, and practical deployment solut

Artificial intelligence inference startup Sail Research Inc. today announced that it has raised 80 million in funding at a 450 million valuation. The company received the bulk of the capital in the fo

Building AI systems at scale is demanding, requiring low-latency inference, fast vector search, strong GPU price-performance and infrastructure that can grow without multiplying operational complexity

Qualcomm Inc.’s stock jumped 14% in after-hours trading today after it shared a series of updates about its artificial intelligence roadmap. The company announced plans to acquire an inference softwar

This IBM Research article discusses optimizing AI inference performance and costs by leveraging mixed hardware configurations, combining different types of processors and accelerators rather than rely

As enterprise AI budgets hit their limits earlier each year, the pressure to fund new agentic and inference workloads without expanding total spend is forcing a fundamental rethink of infrastructure a

DeepSeek is increasing its presence in the AI inference market by competing for higher token volumes, while Anthropic maintains its leadership position in terms of overall spending. This analysis like

Processing millions of user reviews in real-time, under strict latency and cost constraints, is no easy task. Trustpilot has been doing exactly that with custom machine learning since long before larg

Together AI developed an optimized speech-to-text system focused on achieving the fastest processing speeds through technical innovations in their inference stack and model optimization. The approach

Google created MapReduce more than 20 years ago to solve the scaling problems in data processing that the then young company was running into. The AI era that we are in now demands efficient, large-sc

Anthropic are strongly rumored to be about to have their first profitable quarter. Stories are circulating of companies surprised at how expensive their LLM bills are becoming from usage by their staf

Enterprises are adopting an agentic AI PC strategy as Copilot+ PCs shift AI workloads from expensive cloud inference to secure, high-performance on-device processing. As agentic AI moves from experime

Reachy Mini, a robotic system, can now operate with fully local processing capabilities, enabling on-device inference without requiring cloud connectivity. This advancement allows the robot to run AI

Tensormesh Inc. has hit upon a way to make artificial intelligence inference more efficient by eliminating the need for redundant computations, and its technology is so convincing that several of AI i

As enterprises confront spiraling cloud inference bills, on-prem AI computing is emerging as the decisive lever for making agentic AI economically viable at scale. Dell Technologies Inc.’s announcemen

The AI PC is being fundamentally redefined as agentic workloads push the boundaries of what local compute can deliver — and as runaway cloud token costs force enterprises to rethink where inference ac

Today, Amazon SageMaker AI introduces OpenAI-compatible API support for real-time inference endpoints. If you use the OpenAI SDK, LangChain, or Strands Agents, you can now invoke models on SageMaker A

Agentic AI inference at one-tenth the cost per token with NVIDIA Vera Rubin NVL72. Agent sandboxes run 50% faster on NVIDIA Vera than traditional CPUs — while enterprise data queries are up to 3x fast

As enterprises push AI beyond the pilot stage, the cost and complexity of running inference at scale are forcing a fundamental rethink of how infrastructure is designed, governed and sourced, putting

Enterprise AI adoption has crossed a threshold: The question is no longer whether to invest, but how to do it wisely. As agentic workloads multiply and inference costs rise, AI choice — the ability to

We’ve rolled out a significant update to Google Kubernetes Engine (GKE) that solves one of the most annoying problems in cloud infrastructure: cold start latency. GKE now has up to 4x faster node star

Post-training quantization is a technique that reduces model size and improves inference performance by converting weights and activations to lower precision formats after training is complete. NVIDIA

Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model’s parameters is used during each inference. However, to translate this sparsity into practical

AI compute resources should not be treated as interchangeable commodities, as different hardware configurations, providers, and architectures significantly impact training costs, inference latency, an

Featherless.ai Inc., a serverless inference platform startup that hosts open-source artificial intelligence models, today revealed it has raised 20 million in new funding to expand its global infrastr

Dell Technologies’ AI Factory is emerging as a core building block for enterprise AI deployment, delivering full-stack infrastructure for training and inference since its 2024 launch. Yet as AI moves

GitHub is transitioning GitHub Copilot to a usage-based billing model to manage escalating AI inference costs. Effective June 1, subscribers will receive a set allotment of 'AI Credits'

* 본 아티클의 원문은 2026년 4월 23일 Google Cloud 블로그(영문)에 게재되었습니다. AI는 질문에 답하는 수준을 넘어 추론하고 행동하는 단계로 진화하고 있습니다. 오늘날의 에이전틱 시대(agentic era)를 선도하고자 하는 기업에는 이러한 새로운 요구사항에 맞춰 설계되고 최적화된 컴퓨팅 인프라가 필요합니다. 오늘 Google Cloud

Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based architectures, but the sequential nature of their computation h

As AI inference demand surges and enterprise compute budgets buckle under expanding workloads, x86 efficiency has emerged as one of the most powerful — and underutilized — levers in cloud financial ma

Google announced Ironwood, its seventh-generation TPU that is twice as power efficient as the previous generation, alongside specialized hardware designed to support the emerging agentic AI era. Ironw

We built a custom technology stack to run fast large language models on Cloudflare’s infrastructure. This post explores the engineering trade-offs and technical optimizations required to make high-per

Load balancing and scaling LLM serving involves distributing inference requests across multiple model instances or GPUs to prevent bottlenecks and ensure consistent response times under varying traffi

Traditional data centers only stored, retrieved and processed data. In the generative and agentic AI era, these facilities have evolved into AI token factories. With AI inference becoming their primar

Wall Street Journal: Ornn Compute Price Index: renting one Nvidia Blackwell GPU for an hour now costs 4.08, up 48% from 2.75 two months ago, driven by agentic AI demand — AI companies are rationing of

MiniMax M2.7 is an enhancement of the MiniMax M2.5 model, built as a 230B-parameter Mixture-of-Experts (MoE) model with 10B active parameters per token and a 200K context length, designed for agentic

Training LLMs requires periodic checkpoints — full snapshots of model weights, optimizer states, and gradients — whose storage costs can reach $200,000/month for a 405B model on 128 NVIDIA DGX B200...

With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS Cost Explorer and AWS Data Exports. In this post, you will learn how to set up Projects en

NVIDIA’s open‑source Nemotron 3.5 Super VL is a 120 B parameter mixture‑of‑experts multimodal model that activates only 12 B parameters per token, enabling combined reasoning, instruction following, t

Modal Labs Inc., a provider of artificial intelligence infrastructure, today announced that it has raised 750 million in late-stage funding. Accel led the Series D round. It was joined by Kleiner Perk

FactSet leveraged the Databricks AI Runtime to cut end‑to‑end model training and deployment times from days to hours. By adopting high‑performance GPU clusters, managed data pipelines, and automated n

Managing AI model access across dozens or hundreds of AWS accounts creates friction when each account must accept AWS Marketplace Private Pricing Offers individually. This post shows two ways to activ

When we talk about agents, that doesn't necessarily mean we're talking about Python. In today's modern ecosystem, an agent might run as a high-concurrency Go microservice on Cloud Run, or live inside

Silicon chip startup xMEMS Labs Inc. today said it has started mass production of its XMC-2400 and XMC-4800 chips for cooling artificial intelligence hardware. No one has commercialized solid-state ac

Learn how to deploy decision models with llama.cpp on Amazon SageMaker AI. This technical how-to serves OpenAI-compatible chat completions with the AWS Deep Learning Container for llama.cpp, then exte