d-Matrix vs Graphcore

By valuation, d-Matrix is the larger company at $2B versus $500M — a 4x difference. Size can also be measured by team: d-Matrix employs 150 people while Graphcore has 500-1000 employees.

Graphcore has raised more in total funding at $700M, compared to d-Matrix's $450M — a gap of $250M. Combined, the two companies have completed 6 known funding rounds.

Graphcore leads with an Awaira Score of 85/100, while d-Matrix sits at 68/100. That 17-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

d-Matrix was founded by Sid Sheth in 2019. Graphcore was founded by Nigel Toon in 2016. Visit each company's profile on Awaira for a full founder biography.

d-Matrix: d-Matrix is an AI infrastructure company founded in 2019 that develops hardware and software solutions optimized for generative AI and large language model inference. The company designs specialized processors and system architectures to improve the efficiency and cost-effectiveness of deploying AI models at scale. d-Matrix's core technology focuses on reducing latency and power consumption in AI workloads, addressing key bottlenecks in data center operations. The company has raised $450 million across funding rounds, achieving a $2.0 billion valuation as of its Series C stage. d-Matrix competes in the competitive AI infrastructure market alongside companies developing custom silicon and inference acceleration platforms. Its approach targets enterprises and cloud providers requiring optimized inference capabilities for large-scale AI deployments. The company operates within the growing segment of AI infrastructure providers that emerged to support the infrastructure demands of modern generative AI applications. d-Matrix's positioning emphasizes efficiency gains and operational cost reduction compared to standard computing infrastructure. The company addresses a critical market need as organizations seek to deploy AI models economically while maintaining performance requirements. Its technology appeals to data center operators and enterprises managing substantial inference workloads. d-Matrix represents the category of specialized hardware companies built to support the computational demands of contemporary AI systems. The company's trajectory reflects broader industry expansion in AI infrastructure optimization and deployment technologies. d-Matrix specializes in inference optimization hardware specifically designed to reduce the computational and energy costs of deploying large language models at scale. Graphcore: Graphcore designs the Intelligence Processing Unit, a processor architecture built specifically for machine learning workloads, offering a hardware alternative to NVIDIA GPUs for AI model training and inference. The Bristol-based company developed the IPU around a bulk synchronous parallel computation model that distributes model parameters across thousands of processor cores with local memory, achieving high efficiency for sparse and irregular neural network computations that GPUs handle inefficiently.\n\nThe company raised approximately $700 million across six funding rounds including a Series E that valued it at approximately $2.8 billion, with investors including Sequoia Capital, Microsoft, and Samsung Ventures. Graphcore processors are deployed in research institutions including Oxford, Cambridge, and the Rosalind Franklin Institute, as well as commercial AI platforms. The company has shipped multiple IPU generations including the MK2 IPU and Bow IPU, with the Colossus processor and IPU-POD system providing data centre scale AI compute.\n\nGraphcore competes directly against NVIDIA in the AI accelerator market, alongside AMD, Intel Gaudi, and other AI chip startups including Cerebras, SambaNova, and Groq. The AI accelerator market is projected to exceed $100 billion by 2027, driven by demand for model training compute. Graphcore faces the dominant position of NVIDIA and its CUDA software ecosystem as the primary barrier to adoption, requiring significant software investment to match the maturity of CUDA tooling that researchers and engineers have relied on for over a decade.

Graphcore got there first, launching in 2016 — that's 3 years of extra runway. d-Matrix didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

d-Matrix has about 150 employees; Graphcore has about 500-1000. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both d-Matrix and Graphcore compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.