Hello People,
Welcome to the new Edition of Business Analytics Review !!
Meta launches an AI short-form video feed “Vibes,” Clarifai ships a new reasoning engine to speed multimodal inference, CoreWeave inks a $6.5B expansion deal with OpenAI.
Today’s Quick Recaps
What happened: OpenAI announced five new U.S. Stargate data-center sites that bring the project’s planned capacity to nearly 7 gigawatts of compute, via partnerships with Oracle and SoftBank. Read More Here
Why it matters: This materially increases the physical infrastructure available for large models, lowering latency and enabling larger, stateful agent deployments at scale — a direct lever on cost, throughput and model placement decisions for data teams. Read More Here
The takeaway: If your workflows depend on large LLM inference, plan for distributed deployment options (regional endpoints + hybrid cloud) and benchmark for throughput (tokens/sec) and cost per 1M tokens now — those metrics will move as Stargate capacity comes online.
Deep Dive - Why Five New Stargate Sites Matter for Data Teams
OpenAI’s announcement (backed by partners Oracle and SoftBank and supported by major infrastructure deals) pushes Stargate to practically match a new class of on-demand compute capacity: ~7 GW planned. This isn’t just PR — it changes where and how you run inference and training: regional endpoints reduce network egress, and dedicated campuses enable lower-latency cross-GPU model serving. Explore More
The Problem: Cloud latency, variable availability, and rising per-token inference costs make productionizing large models (multi-agent systems, real-time retrieval-augmented apps) expensive and unreliable across regions.
The Solution: A coordinated infrastructure build (Stargate) that adds local capacity, combined with orchestration & hybrid deployments to place model shards and caches near users.
Infrastructure Partnership: OpenAI + Oracle + SoftBank — pooled capital and site selection accelerate capacity rollout and provide anchor customers for large campus builds.
Scale & Power: ~7 GW planned capacity means orders-of-magnitude increases in available GPU-hours for large models, which translates into improved throughput and potential cost-per-inference reductions.
Operational Approach: Expect more regional edge endpoints, integrated networking between cloud providers and on-campus caches to minimize cross-region egress for retrieval-augmented pipelines.
The Results Speak for Themselves:
Baseline: Typical cross-region LLM latency: 200–800 ms per request (varies).
After Optimization: Regional colocated endpoints can lower median latency to <50 ms for many interactive tasks (expected as capacity comes online). (Operational delta depends on topology; monitor with synthetic probes.)
Business Impact: Reduced latency and higher throughput directly increase user engagement in interactive apps and cut per-session compute costs — at scale this can mean millions saved annually for large consumer apps (value depends on QPS and model size).
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What We’re Testing This Week - Optimizing retrieval-augmented inference for lower latency and cost
Brief intro: With new large campus capacity coming online, practical gains come from smarter placement of retrieval caches and quantized models. We’re testing two approaches:
Sharded Local Serving (VShard) — Break the model into smaller shards and serve hot shards on local GPU pools.
Practical tip: pin frequently used attention layers to local memory and offload rarely used layers to regional pools; measure tokens/sec changes.
Quantized + Distilled Edge Models — Use INT8/4 quantization and distilled 6B models as front-line responders, fall back to full model for complex queries.
Practical tip: run A/B latency vs. accuracy on a held-out set; aim for <5% degradation in top-1 accuracy for a 2–4x throughput gain.
Recommended Tools for Today
CoreWeave
GPU cloud for large model training and serving; recently expanded OpenAI pact (large capacity and enterprise procurement options). Reuters
Oracle Cloud Infrastructure (OCI)
Enterprise data center partnership for Stargate sites — strong for colocated campus interconnects and predictable pricing. Data Center Dynamics
Weave/Datadog (monitoring)
Integrate end-to-end latency and token-cost dashboards to measure regional performance and cost per million tokens. DataDog
Quick Poll
3 Things to Know Before Signing Off
• OpenAI/Oracle/SoftBank — Stargate adds five U.S. sites to reach ~7 GW planned capacity; major impact on where you place inference. Read More
• Meta — launched “Vibes,” an AI short-form video feed inside Meta AI to boost AI-generated video creation and distribution. Read More
• CoreWeave — expanded its OpenAI contract (up to $6.5B), strengthening on-demand GPU supply chains. Read More
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