GPU Infrastructure

593 summarised stories about GPU Infrastructure, each linking back to the original source. Browse all topics →

Key players

NVIDIA SiliconANGLE OpenAI Microsoft CoreWeave Google

Sunday, 6 September 2026

Meta FAIR Introduces AI Research Preference Models (RPMs): Ranking ML Experiments Before Spending GPU Hours

MarkTechPost 1 month ago 14

Meta FAIR formalized research preference and introduced AI Research Preference Models (RPMs) to rank unexecuted candidates so an AI research agent executes only the most promising one. The inference-only RPM achieved an average normalized score increase from 0.684 to 0.711, and the agentic RPM reached 0.729 on AIRS-Bench. As a result, the system hit the baseline’s 24-hour results in about 15–15.5 hours and reported new SOTA scores (WinoGrande 94.1% and SVAMP 95.7%).

Read the full story

AI Agents GPU Infrastructure Meta Model Evaluation

Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed

MarkTechPost 1 month ago 22

Perplexity Engineering published Fast Embeddings on GPUs, detailing the GPU serving stack (Ivy, Tulip, and ROSE) that powers pplx-embed and related Perplexity search and ranking models. It reports GPU saturation at around 512 tokens on a sub-billion-parameter embedding model. As a result, embedding serving reuses the LLM prefill/decode kernels and uses CUDA graph + lazy capture and a LazyTensor async path so latency scales mainly with token count rather than sequence count.

Read the full story

AI Infrastructure GPU Infrastructure Model Deployment Embedding Models

Every AI story that matters,
in your inbox by 8am.

TLDRocket reads all relevant sources, removes duplicate coverage, and summarises the day in two minutes. Follow companies and topics for alerts, or get the briefing in Slack. Free, no spam, unsubscribe anytime.