gpu instances
30 articles about gpu instances in AI news
VS Code Now Connects Directly to Google Colab With Free T4 GPU
Google Colab integrates with VS Code, offering a free T4 GPU inside the editor, bypassing cloud GPU providers.
Gur Singh Claims 7 M4 MacBooks Match A100, Calls Cloud GPU Training a 'Scam'
Developer Gur Singh posted that seven M4 MacBooks (2.9 TFLOPS each) match an NVIDIA A100's performance, calling cloud GPU training a 'scam' and advocating for distributed, consumer-hardware approaches.
Cloud GPU vs. Colocation: H100 Costs $8k/Month on Google Cloud vs. $1k Colo
A technical founder highlights the stark economics: renting one H100 on Google Cloud costs ~$8,000/month, while the retail hardware is ~$30,000. At that rate, 4 months of cloud rental equals the cost of outright ownership, making colocation at ~$1k/month a compelling alternative for sustained AI workloads.
AWS Beats Cloud Rivals to NVIDIA Blackwell with EC2 G7 — 4.6x AI Inference Gain Over G6
AWS launched EC2 G7 instances on June 19, 2026, becoming the first major cloud to offer NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. The instances claim 4.6x AI inference performance over G6, backed by 700 Gbps EFA networking and 32 GB GDDR7 per GPU. The move arrives the same week AWS confirme
AWS Never Retired an A100 Server, CEO Says Amid Chip Shortage
AWS CEO Matt Garman stated that A100 servers are completely sold out and never retired, as demand for older chips outpaces supply. This underscores the prolonged GPU shortage and the value of legacy hardware in cloud AI.
Meta Deploys Millions of Amazon Graviton CPUs for AI Agents
Meta will deploy tens of millions of AWS Graviton5 CPU cores for AI agent workloads, signaling that agentic inference favors CPUs over GPUs. The deal deepens Meta's $200B+ infrastructure push amid layoffs and cloud rivalry.
CoreWeave & Google Raise $6.7B in Junk Bonds for AI Infrastructure
Google and GPU cloud provider CoreWeave have jointly raised $6.7 billion through a junk bond offering, with Google taking $5.7 billion. The capital is earmarked for a significant build-out of AI data center infrastructure.
Microsoft's BitNet Enables 100B-Parameter LLMs on CPU, Cuts Energy 82%
Microsoft Research's BitNet project demonstrates 1-bit LLMs with 100B parameters that run efficiently on CPUs, using 82% less energy while maintaining performance, challenging the need for GPUs in local deployment.
Compute Shortage to Split AI Market: Rich Get Agents, Poor Get Chatbots
Mollick warns compute shortage makes agents expensive while chatbots cheapen, splitting AI market by company resources.
Vibe-Coding Bottleneck: CPU Box Rental Gets Harder
SemiAnalysis flags that vibe-coding wave makes cheap CPU box rentals less routine, bottlenecking developers who need quick cloud compute for AI prototyping.
CoreWeave Tops Kimi K2.6 Inference Speed
CoreWeave tops 10 other providers on speed and price-performance for Moonshot AI's Kimi K2.6 in Artificial Analysis benchmark.
Unsloth × NVIDIA Cut LLM Fine-Tuning ~25% — Three Glue-Code Wins on Blackwell
Daniel & Michael Han at Unsloth, in collaboration with NVIDIA, published a joint guide quantifying three glue-code optimizations that combine for ~25% faster LLM training on B200 Blackwell hardware. The wins target overhead around the main kernels — caching packed-sequence metadata, double-buffered gradient checkpoint reloads, and a cheaper GPT-OSS MoE router using argsort + bincount. All three are merged via public PRs.
Oracle Nabs $16B for Michigan AI Data Center, Rivaling Google Cloud
Oracle has secured $16 billion in funding for a massive AI data center in rural Michigan, a move that pits it directly against Google Cloud and other hyperscalers in the race to build AI infrastructure.
AI Fine-Tuning: Why the Technique Matters More Than Which Model You Pick
Sanket Parmar argues that fine-tuning shapes model behaviour for your domain more than base model selection. The article emphasizes that investing in adaptation yields better returns than chasing the latest foundation model.
AI Chip Capacity Crisis: 10GW Left Through 2030, Prices Up Double Digits
The AI accelerator market has only 10 gigawatts of capacity left for contract through 2030, with 100GW already under contract. Prices are rising double digits as one competitor has stopped taking orders entirely.
NVIDIA, Google Cloud Expand AI Partnership for Agentic & Physical AI
NVIDIA and Google Cloud announced an expanded partnership to advance agentic and physical AI, focusing on new infrastructure and software integrations. This builds on their existing collaboration to provide optimized AI training and inference platforms.
Microsoft, Google Shift to Range-Based AI Capacity Planning at DC World 2026
At Data Center World 2026, Microsoft and Google revealed they've shifted from point forecasts to range-based planning for AI workloads, with weekly reviews and modular infrastructure to absorb demand volatility.
Microsoft's Fairwater AI Data Center Launches Early, Boosts Azure Capacity
Microsoft has launched its Fairwater AI data center ahead of schedule. The facility adds significant high-performance computing capacity to Azure's AI infrastructure, crucial for training and running large models.
UALink 2.0 Spec Finalized, Aims to Challenge NVLink for AI Clusters
The UALink 2.0 interconnect specification has been finalized, providing a standardized way to link AI accelerators from AMD, Intel, and others. However, it lags behind NVIDIA's established NVLink technology in real-world deployment.
Google, Marvell in Talks to Co-Develop New AI Chips, Including TPU-Optimized MPU
Google is reportedly in talks with Marvell Technology to co-develop two new AI chips: a memory processing unit (MPU) to pair with TPUs and a new, optimized TPU. This move is a direct effort to bolster Google's custom silicon stack and compete with Nvidia's dominance.
AMD Backs UALink Open Interconnect to Challenge NVIDIA NVLink in AI
AMD is supporting the newly formed UALink Consortium, which aims to create an open standard for connecting AI accelerators. This move challenges NVIDIA's control over the critical NVLink technology that underpins its AI data center systems.
Mac Studio Runs 122B-Parameter AI Model Locally, Beats AWS on Cost
A developer demonstrated that a $3,999 Mac Studio can run a 122B-parameter AI model locally. Compared to a $5/hour AWS instance, the Mac pays for itself in roughly five weeks of continuous use.
Canada's AI Compute Gap: Google Cloud Montreal Offers 2017-Era Chips
A technical developer's attempt to rent modern AI compute in Canada revealed a stark infrastructure gap, with major providers offering chips as old as 2017, undermining national AI ambitions.
AWS Launches 'Generative AI on AWS' Developer Hub
AWS has launched 'Generative AI on AWS,' a new central portal for its AI services, SDKs, and tutorials. This move consolidates its offerings to better compete with Google's Vertex AI and Microsoft's Azure AI Studio.
AI Models Dumber as Compute Shifts to Enterprise, Users Report
Users report noticeable performance degradation in major AI models this month. Analysts suggest providers are shifting computational resources to prioritize enterprise clients over general subscribers.
AWS CEO: All Latest Anthropic Models Trained on Amazon Trainium
Amazon Web Services CEO Matt Garman stated that all of Anthropic's latest AI models are trained on AWS's custom Trainium chips. This confirms the deepening technical and strategic integration between the AI lab and its primary cloud investor.
Intel & Google Announce Multiyear AI & Cloud Infrastructure Partnership
Intel and Google have announced a multiyear strategic collaboration to advance AI and cloud infrastructure, focusing on optimizing Google Cloud for Intel's Xeon processors, Gaudi AI accelerators, and future chips.
Broadcom to Manufacture Google TPU Chips in Foundry Partnership
Google has licensed its Tensor Processing Unit (TPU) intellectual property to Broadcom for chip fabrication. This allows Google to earn from its IP while Broadcom manages the complex hardware build and networking integration.
Gemma 4 Ported to MLX-Swift, Runs Locally on Apple Silicon
Google's Gemma 4 language model has been ported to the MLX-Swift framework by a community developer, making it available for local inference on Apple Silicon Macs and iOS devices through the LocallyAI app.
Azure ML Workspace with Terraform: A Technical Guide to Infrastructure-as-Code for ML Platforms
The source is a technical tutorial on Medium explaining how to deploy an Azure Machine Learning workspace—the central hub for experiments, models, and pipelines—using Terraform for infrastructure-as-code. This matters for teams seeking consistent, version-controlled, and automated cloud ML infrastructure.