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Speedata

Speedata

@Speedata1

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.

Katılım Mayıs 2020
147 Takip Edilen84 Takipçiler
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Speedata
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. linkedin.com/feed/update/ur… #Analytics #SPARK #AI
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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 - speedata.io/workload-analy…
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Ben Bajarin
Ben Bajarin@BenBajarin·
This good framing from Jensen, that we need CPUs created/designed for agents not for the speed of human users. To quote @jpatel41 "humans click, agents swarm."
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Speedata
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 - speedata.io/workload-analy…
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Teng Yan
Teng Yan@tengyanAI·
@lordOfAFew you may not believe it but you’re top 1% early ser
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Speedata
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 -- speedata.io/workload-analy…
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Speedata
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.
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Speedata
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. speedata.io/post/know-your…
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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.”

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Speedata
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 - tinyurl.com/apugpu
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Speedata
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 speedata.io/live-apu-demo. #AI #SQL #Spark
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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: speedata.io/post/one-chip-…
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Speedata
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: lnkd.in/eAJreXaM
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Speedata
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! linkedin.com/event/manage/7…
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Speedata retweetledi
Adi Gelvan
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 tinyurl.com/sr6n9z4h
Intel@intel

Intel is proud to join the Terafab project with @SpaceX, @xAI, and @Tesla to help refactor silicon fab technology. Our ability to design, fabricate, and package ultra-high-performance chips at scale will help accelerate Terafab’s aim to produce 1 TW/year of compute to power future advances in AI and robotics. It was fun hosting @elonmusk at Intel this past weekend!

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Speedata
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 -linkedin.com/events/7429495…
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 linkedin.com/events/7429495…

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Keşfet

@BenBajarin @jpatel41 @tengyanAI @lordOfAFew @eliadeleo @GoldmanSachs @IAmAdiFuchs @elonmusk