A Meta spokesperson confirmed the hirings in response to a
request for comment, after Reuters identified 10 people whose LinkedIn profiles
said they worked at Graphcore until December 2022 or January 2023 and
subsequently joined Meta in February or March of this year.
"We recently welcomed a number of highly-specialized
engineers in Oslo to our infrastructure team at Meta. They bring deep expertise
in the design and development of supercomputing systems to support AI and
machine learning at scale in Meta's data centers," said Jon Carvill, the
Meta spokesperson.
The move brings additional muscle to the social media
giant's bid to improve how its data centers handle AI work, as it races to cope
with demand for AI-oriented infrastructure from teams across the company
looking to build new features.
Meta, which owns Facebook and Instagram, has become increasingly
reliant on AI technology to target advertising, select posts for its apps'
feeds and purge banned content from its platforms.
On top of that, it is now rushing to join competitors like
Microsoft and Alphabet's Google in releasing generative AI products capable of
creating human-like writing, art and other content, which investors see as the
next big growth area for tech companies.
The 10 employees' job descriptions on LinkedIn indicated the
team had worked on AI-specific networking technology at Graphcore, which
develops computer chips and systems optimized for AI work.
Carvill declined to say what they would be working on at
Meta.
Graphcore closed its Oslo office as part of a broader
restructuring announced in October last year, a spokesperson for the startup
said, as it struggled to make inroads against US-based firms like Nvidia and
Advanced Micro Devices which dominate the market for AI chips.
Meta already has an in-house unit designing several kinds of
chips aimed at speeding up and maximizing efficiency for its AI work, including
a network chip that performs a sort of air traffic control function for
servers, two sources told Reuters.
Efficient networking is especially useful for modern AI
systems like those behind chatbot ChatGPT or image-generation tool Dall-E,
which are far too large to fit onto a single computing chip and must instead be
split up over many chips strung together.
A new category of network chip has emerged to help keep data
moving smoothly within those computing clusters. Nvidia, AMD and Intel all make
such network chips.
In addition to its network chip, Meta is also designing a
complex computing chip to both train AI models and perform inference, a process
in which the trained models make judgments and generate responses to prompts,
although it does not expect that chip to be ready until around 2025.
Graphcore, one of the UK's most valuable tech startups, once
was seen by investors like Microsoft and venture capital firm Sequoia as a
promising potential challenger to Nvidia's commanding lead in the market for AI
chip systems.
However, it faced a setback in 2020 when Microsoft scrapped
an early deal to buy Graphcore's chips for its Azure cloud computing platform,
according to a report by UK newspaper The Times. Microsoft instead used
Nvidia's GPUs to build the massive infrastructure powering ChatGPT developer
OpenAI, which Microsoft also backs.
Sequoia has since written down its investment in Graphcore
to zero, although it remains on the company's board, according to a source familiar
with the relationship. The write-down was first reported by Insider in October.
The Graphcore spokesperson confirmed the setbacks, but said
the company was "perfectly positioned" to take advantage of
accelerating commercial adoption of AI. © Reuters
0 comments:
Post a Comment