Speedata

The Analytics Processing Unit (APU) — purpose-built silicon for AI data prep, Apache Spark SQL & batch ETL. 100x faster. 90% lower TCO. Zero code changes.

[Speedata @Speedata1]

Analytics and AI Data is about to have its #GPU moment. Workloads like AI, video, and databases already made the leap to specialized hardware, analytics is next. 50–100× faster performance. Up to 90% lower cost. The APU is here. https://t.co/gpnUbwQC8g
#Analytics #SPARK #AI

[Speedata @Speedata1]

@BenBajarin @jpatel41 The foundation for enterprise AI agents is structured enterprise data. Our APUs can deliver 1-2 orders of magnitude better performance, price-performance and energy efficiency for SQL analytics compared to CPU and GPU. We'd love to show you - https://t.co/J1pQHzpKzv

[Speedata @Speedata1]

@tengyanAI @lordOfAFew YES. It's the $$$ line item nobody's modeling yet - what it costs to run analytics once agents, not people, are driving the query volume. The Analytics Processing Unit (APU) was built for this. We'd love you to test our Workload Analyzer - https://t.co/J1pQHzpKzv

[Speedata @Speedata1]

@eliadeleo @GoldmanSachs YES. And our APUs can deliver 1-2 orders of magnitude better performance, price-performance and energy efficiency for SQL analytics compared to CPU and GPU. Try out Workload Analyzer, would love to hear your thoughts -- https://t.co/J1pQHzpKzv

[Speedata @Speedata1]

AI agent token use will grow 24x by 2030, generating more #SQL queries than humans @GoldmanSachs. The foundation for enterprise AI agents is structured data, it's why #OpenAI & #Anthropic partner with #Databricks & #Snowflake. But APUs beat CPU & GPU by 1-2 orders of magnitude.

[Speedata @Speedata1]

The limiting factor in #VLSI design isn't model strength, it's whether the agent is operating inside your workflow and your context. @IAmAdiFuchs published a field guide to making #AI coding agents actually useful in hardware design now. https://t.co/xHr7rzhUOw

[Speedata @Speedata1]

Speedata is hiring a Software Engineering Manager, Performance Modeling.

Purpose-built silicon. Purpose-built careers. Join us. Email [email protected] or https://t.co/MPRTLwdife

[Speedata @Speedata1]

@elonmusk is correct.

[Michael Granoff @mikejgr]

.@elonmusk speaking live:
“I’m a huge admirer of the innovation coming out of Israel, it is objectively true that Israel punches high above its weight — I think honestly number one in the world… innovation per capita, Israel is by far number one in the world.”

[Speedata @Speedata1]

Model training had scaling laws. A clear improvement trajectory. Data pipelines don't, and it's a big reason most enterprise #AI pilots quietly fail. Our CEO @gelvan_adi goes deeper on it with @DanielNenni on https://t.co/ZdfVGyOLxM

https://t.co/1qbolGL8oC

[Speedata @Speedata1]

The Speedata Workload Analyzer projects how much faster your real analytics or AI data prep pipelines would run on the Analytics Processing Unit (APU).

Try it: https://t.co/PmybKOErMx

#AnalyticsAcceleration #CloudCost #FinOps #AIDataPrep #MLOps #Semiconductors #AI #CPU #GPU

[Speedata @Speedata1]

Why does a #GPU running SQL feel like it's barely trying? What does an LPU 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. Learn the difference - https://t.co/ByX7MyoRdD

[Speedata @Speedata1]

Speedata is hiring: Lead SoC Architect.

Own the architecture of an ASIC built from the ground up for #analytics and AI data prep, not a repurposed processor.

[email protected] or apply for open #engineering roles here: https://t.co/qkqLVhLReM

#hiring #Israel #startup #AI

[Speedata @Speedata1]

Every Wednesday we host a session for anyone who wants to see the Analytics Processing Unit (APU) in action. We spend 20 minutes running Spark SQL or AI data prep workloads on the APU, walk through the architecture, and Q&A. Register https://t.co/HnOcGEsn3H.

#AI #SQL #Spark

[Speedata @Speedata1]

The GPU-first model made sense when AI was experimental. In production, efficiency is the priority. Running the wrong workload on the wrong chip means overpaying in power, memory, and infrastructure costs.

We broke down the AI Ops pipeline: https://t.co/ql97erL3mb

[Speedata @Speedata1]

Speedata is looking for a Board Designer. This role is a great fit for someone who wants to take full ownership of board development and play a key part in building our products. Apply here: https://t.co/xd6JJ0qiBF

#semiconductors #Boarddesign #engineering #hiring #AI #gpu

[Speedata @Speedata1]

AI agents ask analytics questions. But can your infrastructure answer them quickly? Agentic Analytics, executing advanced analytics queries from an LLM is only useful if the answer comes back fast. We discuss where the pipeline bottleneck lives.
Recording: https://t.co/LPaXlKAdmZ

[Adi Gelvan @gelvan_adi]

Pointing AI at the repo isn't enough.

@Speedata1's AI expert @IAmAdiFuchs breaks down how we used a UART-to-AXI bridge to test AI in VLSI verification, and what it actually takes to make it work.

https://t.co/4xSyxqMQN1

#VLSI #ChipVerification #UVM #AIEngineering #AI

[Speedata @Speedata1]

Speedata webinar, "One Chip Can't Do It All: The New #AI Tech Stack," is tomorrow 1PM EST.

#GPUs, TPUs, LPUs, APUs - each one was built for a different job. We're breaking down where each processor fits in the AI compute pipeline - join us! https://t.co/piGTLZV1Ip

[Adi Gelvan @gelvan_adi]

@LipBuTan1, a @Speedata1 investor, is leading @intel's partnership w @elonmusk to reimagine chip manufacturing. At Speedata, he's backing our purpose-built silicon, the APU, to accelerate the massive #Spark, ETL, and #AI data prep workloads.
Learn more https://t.co/wqqGvH68cu

[Speedata @Speedata1]

Join our webinar, "One Chip Can't Do It All: The New AI Tech Stack" on April 14 - we'll break down where each processor, APUs, GPUs, LPUs and TPUs fit in your stack. Register here -https://t.co/imGQT5Gam7

[Adi Gelvan @gelvan_adi]

If you're still running #AI workloads on general-purpose hardware like GPUs for everything, CPUs maxed out on Spark, this is the session where @Speedata1 breaks down where APUs, #GPUs, #TPUs and #LPUs fit in your tech stack. April 14, live. Register here https://t.co/QBZFlyN4PS

[Liraz Blumenkrantz Menchell @LirazM]

A jar of #Nutella just traveled farther than any human in history.

Meanwhile, some #queries are still running… somewhere