Speedata.io

Most of the AI spend conversation is about models and GPUs. The $$$ hefty line item nobody's modeling yet is what it costs to run analytics once agents, not people, are driving the query volume. That bill scales fast, and it doesn't scale on the hardware most enterprises are running today. Test our Workload Analyzer to see what your SQL analytics actually cost on the Analytics Processing Unit (APU) versus CPU and GPU. Link in comments. #AI #AIAgents #CPU #GPU

Most organizations are bolting AI onto infrastructure built for humans. It works as a starting point, but it's a ceiling, not a strategy. Our AI expert, Adi Fuchs lays out the practical rules for running AI coding agents inside real VLSI environments: skills, explicit context, traceability, minimal diffs, evidence-based gating. The stuff that separates AI "help" from actual engineering capability. But the bigger shift is "Build for AI": redesigning the VLSI process from first principles so human engineers and AI agents collaborate natively. Machine-readable artifacts by default. Context and state as the primary, not legacy knowledge scattered across scripts, wikis, and Slack. 9 practical rules, including the one most teams skip - define your human-only decisions up front. Let agents draft fixes and scaffolding. Reserve architecture, CDC sign-off, and tapeout readiness for people. Read more on our blog - link in comments.

Speedata is growing by leaps and bounds. This week's role: Software Engineering Manager, Performance Modeling. Lead the team building the simulators that serve as the source of truth for our ASIC, functional and performance models that drive architectural exploration. What you'll do: - Lead and grow a team of experienced engineers building hardware simulators from the ground up - Drive co-design initiatives across Architecture, VLSI, and Software teams - Provide insights that shape chip architecture, compiler optimization, and system-level performance Purpose-built silicon. Purpose-built careers. Join us. Email recruit@speedata.io View full role at link in the comments. #AI #Semiconductor #PerformanceModeling #hiring #GPU #CPU

Read NVIDIA's own workload list for Vera. "Data analytics" is right there next to orchestration and tool-calling. They just told the market the analytics layer needs purpose-built silicon. That's Speedata's analytics processing unit - the APU = data analytics. See how much faster your Spark workloads run on the APU - https://lnkd.in/eJdEMRQh

Model training had scaling laws. A clear improvement trajectory. Data pipelines don't. No roadmaps, no best practices, and it's a big reason most enterprise AI pilots quietly fail. A deployed model only knows what it was trained on, public data or whatever fixed dataset it shipped with. Making it valuable inside an enterprise means feeding it the company's own structured data, cleanly. This is the gap behind most failed AI pilots, and the workload our APU is built for. Our CEO Adi Gelvan goes deeper on it with Daniel Nenni at SemiWiki.com. https://lnkd.in/eJmnrwFT #AI #Analyticsacceleteration #Spark #AIDataprep #ETL

TRY THE WORKLOAD ANALYZER: The Speedata Workload Analyzer projects how much faster your real analytics workloads or AI data prep pipelines would run on the Analytics Processing Unit (APU). Three ways to test: 🟧 Upload a Spark event log in the browser - results in minutes 🟧 Run our CLI locally - your logs never leave your environment 🟧 Compare against TPC-DS benchmarks How much faster would your Spark workloads run on dedicated analytics silicon? Link in comments to try it out. #AnalyticsAcceleration #CloudCost #FinOps #AIDataPrep #MLOps #Semiconductors #AI #CPU #GPU

The server price tsunami is here. Memory-heavy configs are quoting 3–4x higher than six months ago, with delivery timelines stretching past six months. Bamiyan Gobets lays out the real choice for infrastructure leaders running Apache Spark - pay the new market price for fewer or weaker servers, or architect your way out by accelerating the workloads already consuming the most infrastructure. Speedata's APU strategy: free up 80–90% of your existing Spark estate, or buy 80–90% fewer servers for new expansion. Either path protects the budget without compromising performance. A must-read for any Head of Infrastructure heading into 2027+ planning. #AI #GPU #CPU #Semiconductors

Why does a GPU running SQL feel like it's barely trying? Why are TPUs cloud-only? What does an LPU actually do that a GPU can't? The architectures are different because the workloads are different, and at production scale, those differences compound into real money. A breakdown of the five processors defining modern compute, and where each one fits at the link in the comments. #semiconductors #AI #APU #CPU #TPU #LPU #GPU #Nvidia #Google

Every Wednesday we host a live session on the Speedata APU: 20 minutes of Apache Spark SQL and AI data prep workloads executing on purpose-built silicon, plus an architecture walk-through. Today's session is at 1pm EST. To join, register at the link in comments. #AI #Analyticsacceleration #Apachespark

Speedata is growing fast. This week's role: Lead SoC Architect. Own the architecture of an ASIC built from the ground up for analytics and AI data prep, not a repurposed processor. What you'll do: - Define hardware specifications for subsystems across the SoC - Break down sub-system architecture into microarchitectural blocks with the related firmware and SW components - Design the SoC to fit precisely into the datacenter systems it integrates with Purpose-built silicon. Purpose-built careers. Join us. Email recruit@speedata.io View full role at link in the comments. #AI #Semiconductor #SoCarchitect #hiring