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OpenAI builds its own AI chip with Broadcom

OpenAI builds its own AI chip with Broadcom

New Capabilities

Jalapeño, OpenAI's first custom processor, is built to serve models for about half the cost of standard GPUs.

June 25th, 2026: Wall Street reacts: Broadcom gains, Nvidia slips

Overview

Every time you ask ChatGPT a question, OpenAI rents the chips that answer it, mostly from Nvidia. On June 24, OpenAI and Broadcom unveiled Jalapeño, OpenAI's first chip of its own, built to do that work for roughly half the cost.

The chip only runs models, it doesn't train them. But running models is where the money goes once hundreds of millions of people use a product daily. Owning that hardware lets OpenAI cut its single largest cost and lean less on Nvidia.

Why it matters

The cost of running AI sets the price of every chatbot, coding tool, and assistant. Cut it in half and the economics of the whole industry shift.

Questions about this story

0

Do they have plans to deploy them? If so, when?

Yes — Jalapeño goes into initial deployment by end of 2026, with Broadcom and OpenAI committed to scaling to 10 gigawatts of capacity through 2029.

Why it matters: This is when OpenAI actually starts capturing the promised cost savings, shifting from renting Nvidia compute to running on its own silicon.

  • Initial deployment is targeted for late 2026, with the chip designed in just nine months.
  • The October 2025 deal with Broadcom set a 10-gigawatt deployment target running from late 2026 through 2029.
  • Data centers for the rollout are being built with Microsoft and other partners.
  • Jalapeño only handles inference (serving models), so Nvidia GPUs remain in play for training — the deployment plan doesn't replace Nvidia entirely.
Sources
OpenAI CNBC
Room for disagreement
  • Custom chip rollouts routinely slip; OpenAI has no prior track record shipping silicon at scale, so whether late 2026 holds is an open question — though neither Broadcom nor OpenAI has publicly flagged delays.
AI-generated with web search — may be wrong. Check the linked sources.
0

What are the specs of the chip? Give me the details!

Jalapeño is a reticle-sized inference ASIC built on TSMC's 3nm process with eight HBM memory sites — purpose-built for LLM serving, not training.

Why it matters: The spec choices (expensive HBM, maximum die size, 3nm node) show OpenAI is prioritizing raw throughput and latency over cheap hardware — a bet that owning the best inference silicon beats renting GPUs at scale.

  • Built on TSMC's 3nm process — the same node as leading-edge GPUs — which OpenAI says is key to hitting close to theoretical peak utilization.
  • Eight HBM memory stacks surround the central compute die: HBM is far faster than cheaper DRAM alternatives and addresses the memory-bandwidth bottleneck that limits GPU efficiency on LLM workloads.
  • Reticle-sized die: the largest single die a fab can print, maximizing transistor count per chip.
  • Uses a systolic array architecture — efficient for the matrix math that dominates inference — developed by a team led by Richard Ho, a former Google chip engineer.
  • OpenAI and Broadcom have not disclosed peak FLOPS, memory bandwidth numbers, or power draw; a detailed technical paper is expected in coming months.
  • Designed in nine months — claimed to be the fastest ASIC development cycle ever for a high-performance advanced-node chip.
Sources
Tom's Hardware VentureBeat OpenAI
Room for disagreement
  • OpenAI calls Jalapeño 'the best inference platform for LLMs' — but Nvidia's Vera Rubin delivers 50 petaflops per GPU at one-tenth the cost per token (per Nvidia's own GTC 2026 claims), and independent benchmarks haven't yet compared the two head-to-head.
  • Some analysts argue that without disclosed FLOPS or bandwidth numbers, the '50% cost reduction' claim is unverifiable until Jalapeño runs in production at scale.
AI-generated with web search — may be wrong. Check the linked sources.
Play on this story Voices Debate Who Said What? Predict Higher or Lower

Key Indicators

~50%
Lower serving cost
Reported cut in inference cost versus standard AI GPUs, pending an independent technical report.
9 months
Design to tape-out
Time from start to a finished design ready for manufacturing, sped up using OpenAI's own models.
10 GW
Total deal scale
Custom accelerators OpenAI plans to deploy with Broadcom through 2029, enough to power several large data centers.
~40%
Microsoft's expected share
Portion of the chips Microsoft is reported to plan to buy.

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People Involved

Greg Brockman
Greg Brockman
Leading OpenAI's push to design its own hardware
 Hock Tan
Hock Tan
Manufacturing partner for OpenAI's chip
 Jensen Huang
Jensen Huang
Faces a major customer building an alternative

Organizations Involved

OpenAI
OpenAI
Artificial Intelligence Company
Designed its first chip

The maker of ChatGPT, now designing its own processors to lower the cost of running its models.
Broadcom Inc.
Broadcom Inc.
Semiconductor and infrastructure company
Builds and ships the chip

The chipmaker turning OpenAI's design into silicon and supplying the networking to connect the chips at data-center scale.

Timeline

September 2025 June 2026

4 events Latest: June 25th, 2026 · 3 weeks ago
Tap a bar to jump to that date

  1. Wall Street reacts: Broadcom gains, Nvidia slips

    Latest Market

    Broadcom shares rose about 2% the day after the Jalapeño unveil. Nvidia fell roughly 0.75%. JPMorgan kept its Overweight rating on Broadcom with a $580 price target; UBS maintained its Buy rating.

  2. OpenAI and Broadcom unveil Jalapeño

    Product

    OpenAI reveals its first custom chip, built to serve models for about half the cost of standard GPUs. Designed in nine months, it is set for initial deployment by the end of 2026.

  3. OpenAI and Broadcom announce the 10-gigawatt chip plan

    Deal

    The two say OpenAI will design custom accelerators and Broadcom will build and deploy them, starting in late 2026 and running through 2029. Broadcom shares jumped.

  4. OpenAI and Nvidia announce a multi-gigawatt deal

    Deal

    OpenAI and Nvidia unveil a partnership to deploy large amounts of Nvidia compute, reportedly worth up to $100 billion.

Historical Context

2 moments from history that rhyme with this story — and how they unfolded.

May 2016

Google builds the TPU (2015)

Google revealed it had quietly built its own AI chip, the Tensor Processing Unit, to run its machine-learning workloads. It had been using the chips in its data centers for about a year before saying so. The goal was the same as OpenAI's: serve models faster and cheaper than off-the-shelf hardware.

Then

Google cut its cost of running AI services and reduced its dependence on outside chip suppliers.

Now

The TPU became a core part of Google Cloud and proved a big AI company could design competitive silicon in-house.

Why this matters now

Jalapeño follows the same playbook a decade later. It shows custom inference chips can pay off, and that the hard part is volume manufacturing, not the first design.

November 2020

Apple drops Intel for its own silicon (2020)

Apple replaced Intel processors in its Mac computers with chips it designed itself, the M1. After years of buying from Intel, Apple decided controlling the chip let it tune hardware and software together for better speed and battery life.

Then

The first M1 Macs beat Intel models on speed and efficiency, and Apple's margins improved.

Now

Apple now designs the chips in nearly all its products, and Intel lost a marquee customer.

Why this matters now

OpenAI's pitch echoes Apple's: design the chip and the model together and you get gains a general-purpose supplier can't match. The risk for Nvidia is losing a top customer the way Intel lost Apple.

Sources

(11)
+5
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