
Graphcore
Founded Year
2016Stage
Incubator/Accelerator | AliveTotal Raised
$682MRevenue
$0000About Graphcore
Graphcore provides AI systems and services that enable organizations to build, train and deploy models in the cloud using infrastructure processing unit (IPU) hardware. Its products include cloud infrastructure processing units, data center infrastructure processing units, bow infrastructure processing units, and more. The company serves the finance, biotech, scientific research, and consumer internet sectors. It was founded in 2016 and is based in Bristol, United Kingdom.
ESPs containing Graphcore
The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.
The AI infrastructure market provides the necessary hardware, software, and services to support artificial intelligence (AI) workloads and applications. The market provides specialized and expensive processors such as GPUs for running a large number of highly compute-intensive training models in parallel. IT leaders, MLOps, and data science teams face challenges in controlling and managing these r…
Graphcore named as Leader among 10 other companies, including Run:AI, Syntiant, and Deeplite.
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Research containing Graphcore
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CB Insights Intelligence Analysts have mentioned Graphcore in 3 CB Insights research briefs, most recently on May 4, 2021.

Expert Collections containing Graphcore
Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.
Graphcore is included in 5 Expert Collections, including Unicorns- Billion Dollar Startups.
Unicorns- Billion Dollar Startups
1,214 items
AI 100
499 items
Game Changers 2018
70 items
The Edge Computing Landscape
343 items
Edge computing companies facilitate workload deployment in addition to providing data processing and storage at the farthest reaches of the network. These edge computing companies range from data centers at the edge to workload management tools designed to orchestrate edge deploy
Artificial Intelligence
10,627 items
This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.
Graphcore Patents
Graphcore has filed 166 patents.
The 3 most popular patent topics include:
- Parallel computing
- Microcontrollers
- Instruction processing

Application Date | Grant Date | Title | Related Topics | Status |
---|---|---|---|---|
2/12/2020 | 5/16/2023 | Artificial neural networks, Artificial intelligence, Neural networks, Computational neuroscience, Machine learning | Grant |
Application Date | 2/12/2020 |
---|---|
Grant Date | 5/16/2023 |
Title | |
Related Topics | Artificial neural networks, Artificial intelligence, Neural networks, Computational neuroscience, Machine learning |
Status | Grant |
Latest Graphcore News
May 9, 2023
Meta buys AI networking chip team from Graphcore Meta buys AI networking chip team from Graphcore Meta Platforms has hired an Oslo-based team that until late last year was building AI networking technology at Graphcore. Dado Ruvic/Reuters Meta Platforms has hired an Oslo-based team that until late last year was building artificial intelligence networking technology at British chip unicorn Graphcore. A Meta spokesman 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 specialised 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 centres,” said Jon Carvill, the Meta spokesman. They bring deep expertise in the design and development of supercomputing systems to support AI The move brings additional muscle to the social media giant’s bid to improve how its data centres handle AI work, as it races to cope with demand for AI-orientated 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 such as Microsoft and 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 optimised for AI work. Carvill declined to say what they would be working on at Meta. Restructuring Graphcore closed its Oslo office as part of a broader restructuring announced in October last year, a spokesman for the start-up said, as it struggled to make inroads against US-based firms such as Nvidia and AMD, which dominate the market for AI chips. Meta already has an in-house unit designing several kinds of chips aimed at speeding up and maximising efficiency for its AI work, including a network chip that performs a sort of air traffic control function for servers, two sources said. 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 start-ups, 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 Graphcore spokesman confirmed the setbacks, but said the company was “perfectly positioned” to take advantage of accelerating commercial adoption of AI. Graphcore was last valued at US$2.8-billion after raising $222-million in its most recent investment round in 2020. — Katie Paul, Krystal Hu and Stephen Nellis, (c) 2023 Reuters
Graphcore Frequently Asked Questions (FAQ)
When was Graphcore founded?
Graphcore was founded in 2016.
Where is Graphcore's headquarters?
Graphcore's headquarters is located at 11-19 Wine Street, Bristol.
What is Graphcore's latest funding round?
Graphcore's latest funding round is Incubator/Accelerator.
How much did Graphcore raise?
Graphcore raised a total of $682M.
Who are the investors of Graphcore?
Investors of Graphcore include Tech Nation Future Fifty, Molten Ventures, Baillie Gifford & Co., Fidelity International, Ontario Teachers' and 28 more.
Who are Graphcore's competitors?
Competitors of Graphcore include ChipIntelli, Intellifusion, Mythic, ArchiTek, Deci AI, Pensando Systems, Cerebras Systems, OctoML, Blaize, Untether AI and 18 more.
Compare Graphcore to Competitors

Groq designs the Tensor Streaming Processor (TSP) architecture-based chip. It is a single enormous processor that has hundreds of functional units. Its architecture reduces instruction-decoding overhead and handles integer and floating-point data. It was founded in 2016 and is based in Mountain View, California.
Mythic develops a local artificial intelligence (AI) platform. It offers solutions for smart cities and spaces, intelligent machines and robots, smart homes, metaverse, drones, and aerospace industries. The company was formerly known as Isocline Engineering Corp. It was founded in 2012 and is based in Austin, Texas.

Cerebras is a computer systems company dedicated to accelerating deep learning. The Wafer-Scale Engine (WSE), is at the heart of the deep learning system, the Cerebras CS-1. The WSE delivers more computing more memory, and more communication bandwidth. This enables AI research at high speeds and scale.

TensTorrent develops a high-performance processor ASICs, specifically engineered for deep learning and smart hardware. The company's processor is designed to excel at both learning and inference, while being software-programmable to support the future in the field of machine learning. The processor's architecture easily scales from battery-powered IoT devices to large cloud servers, and surpasses today's solutions by several orders of magnitude in raw performance and energy efficiency.

Blaize provides a software platform to span the complete edge artificial intelligence (AI) operational workflow from idea to deployed application. It optimizes artificial intelligence (AI) with the location of data. The platform collects and processes data from the edge to the core, with a focus on automotive, smart vision, and enterprise computing markets. It was formally known as ThinCI. It was founded in 2010 and is based in El Dorado Hills, California.
Another Brain creates chips with integrated artificial intelligence. Its chipsets allow any type of sensor to understand its environment and learn from it, in an autonomous and unsupervised manner. Its markets are intelligent cars, industrial robotics, and drones.
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