Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face

Introducing GPT-6.1 Terra

Bringing fast and capable computer use to everyone

Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines

arXiv:2605. 25645v2 Announce Type: replace-cross Abstract: We present the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on TPU hardware, providing an empirical comparison of TPU and GPU platforms for large language model adaptation.

Fine-tuning now available for GPT-4o

Accelerator Choice Is Not Enough: AlphaFold2 Inference on Cloud TPUs

arXiv:2609.34818v2 Announce Type: replace-cross Abstract: AlphaFold2 is written in JAX, so the same inference code compiles and runs unchanged on CPUs, GPUs and Google Cloud TPUs. That portability ma...

New models and developer products announced at DevDay

GPT-4 Turbo with 128K context and lower prices, the new Assistants API, GPT-4 Turbo with Vision, DALL·E 3 API, and more.

Advancing price-performance for developers with GPT‑5.6 in Kiro

OpenAI has released GPT‑5.6 in Kiro, a new version aimed at improving price-performance for developers. The update assists developers in planning, building, reviewing, and testing software more efficiently. By integrating GPT‑5.6 into the Kiro platform, users can expect a more cost-effective and powerful toolset for their development workflows.

Building for an Open Future - our new partnership with Google Cloud

From GPU to TPU with Coding Agents: What Works, What Fails, and What It Costs?

The study investigates whether coding agents can reliably migrate PyTorch machine learning projects from GPUs to TPUs. Using Claude Code, ten projects were migrated, with eight meeting the project‑goal criterion but 76% of execution attempts failing, often due to compilation issues or silent changes in computation. The research highlights significant human effort (average 2.4 interventions) and variable costs, noting that while some TPUs can be faster and cheaper, failures and accuracy drops remain common.

Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design

arXiv:2606. 10493v1 Announce Type: cross Abstract: Local deployment of large Mixture-of-Experts (MoE) models falls short of the service quality achieved in cloud-scale environments, even under low-concurrency workloads.

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination.

A Measurement Study of LLM Inference Trade-offs Across Edge Continuum Hardware

arXiv:2609.08307v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires bala...

Democratizing MoE inference on commodity GPUs with CoMoE

The paper introduces CoMoE, a communication‑efficient Mixture‑of‑Experts inference system designed for commodity GPUs. By using host‑centric routing, host‑backed token multicast, and fine‑grained token‑level aggregation, CoMoE reduces communication volume and eliminates global synchronization stalls. Experiments on RTX 5090 GPUs show up to 1.46× throughput improvement, approaching NVLink‑capable A800 performance while costing only 23.4% of the hardware.