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Founded Year



Incubator/Accelerator | Alive

Total Raised


Mosaic Score

+90 points in the past 30 days

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The Mosaic Score is an algorithm that measures the overall financial health and market potential of private companies.

About Graphcore

Graphcore provides AI systems and services that enable organizations to build, train and deploy their models in the cloud using the IPU hardware. Its products include cloud IPUs, data center IPUs, Bow IPU, and Poplar. The company serves the finance, biotech, scientific research, and consumer internet sectors. Graphcore was founded in 2016 and is based in Bristol, U.K.

Graphcore Headquarters Location

11-19 Wine Street

Bristol, England, BS1 2PH,

United Kingdom


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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 6 Expert Collections, including Digital Health.


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Artificial Intelligence

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Graphcore Patents

Graphcore has filed 138 patents.

The 3 most popular patent topics include:

  • Parallel computing
  • Instruction processing
  • Microcontrollers
patents chart

Application Date

Grant Date


Related Topics




Microcontrollers, Artificial neural networks, Machine learning, Parallel computing, Artificial intelligence


Application Date


Grant Date



Related Topics

Microcontrollers, Artificial neural networks, Machine learning, Parallel computing, Artificial intelligence



Latest Graphcore News

Intel, AMD and Nvidia propose new standard to make AI processing more efficient

Sep 14, 2022

In pursuit of faster and more efficient AI system development, Intel, AMD and Nvidia today published a draft specification for what they refer to as a common interchange format for AI. While voluntary, the proposed “8-bit floating point (FP8)” standard, they say, has the potential to accelerate AI development by optimizing hardware memory usage and work for both AI training (i.e., engineering AI systems) and inference (running the systems). When developing an AI system, data scientists are faced with key engineering choices beyond simply collecting data to train the system. One is selecting a format to represent the weights of the system — weights being the factors learned from the training data that influence the system’s predictions. Weights are what enable a system like GPT-3 to generate whole paragraphs from a sentence-long prompt, for example, or DALL-E 2 to create photorealistic portraits from a caption. Common formats include half-precision floating point, or FP16, which uses 16 bits to represent the weights of the system, and single precision (FP32), which uses 32 bits. Half-precision and lower reduce the amount of memory required to train and run an AI system while speeding up computations and even reducing bandwidth and power usage. But they sacrifice some accuracy to achieve those gains; after all, 16 bits is less to work with than 32. Many in the industry — including Intel, AMD and Nvidia — are coalescing around FP8 (8 bits) as the sweet spot, however. In a blog post, Nvidia senior group product marketing manager Shar Narasimhan notes that the aforementioned proposed format, which is FP8, shows “comparable accuracy” to 16-bit precisions across use cases including computer vision and image-generating systems while delivering “significant” speedups. Nvidia, Arm and Intel say they’re making their FP8 format license-free, in an open format. A whitepaper describes it in more detail; Narasimhan says that the specs will be submitted to the IEEE, the professional organization that maintains standards across a number of technical domains, for consideration at a later date. “We believe that having a common interchange format will enable rapid advancements and the interoperability of both hardware and software platforms to advance computing,” Narasimhan. The trio isn’t pushing for parity out of the goodness of their hearts, necessarily. Nvidia’s GH100 Hopper architecture natively implements FP8, as does Intel’s Gaudi2 AI training chipset. For its part, AMD is expected to support FP8 in its upcoming Instinct MI300A APU. But a common FP8 format would also benefit rivals like SambaNova, Groq, IBM, Graphcore and Cerebras — all of which have experimented with or adopted some form of FP8 for system development. In a blog post this July, Graphcore co-founder and CTO Simon Knowles wrote that the “advent of 8-bit floating point offers tremendous performance and efficiency benefits for AI compute,” asserting that it’s also “an opportunity” for the industry to settle on a “single, open standard” rather than ushering in a mix of competing formats.

Graphcore Web Traffic

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Graphcore Rank

  • 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., Schroders, Fidelity International and 27 more.

  • Who are Graphcore's competitors?

    Competitors of Graphcore include ArchiTek, Pensando Systems, Cerebras Systems, Blaize, Mythic, ChipIntelli, TensTorrent, DeepCube, Groq, SambaNova Systems and 13 more.

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