Untether AI vs Graphcore

Graphcore has a disclosed valuation of $600M, while Untether AI's valuation is not publicly available, making a direct size comparison difficult. Graphcore employs 500-1000 people.

Graphcore has raised more in total funding at $767M, compared to Untether AI's $160M — a gap of $607M. Combined, the two companies have completed 4 known funding rounds.

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

Untether AI was founded by Martin Snelgrove in 2018. Graphcore was founded by Nigel Toon in 2016. Visit each company's profile on Awaira for a full founder biography.

Untether AI: Untether AI designs AI inference accelerator chips using a compute-near-memory architecture that places processing elements directly adjacent to memory storage rather than in a separate compute chip, dramatically reducing the data movement bottleneck that limits inference throughput and energy efficiency in conventional von Neumann architectures. The Toronto company develops both the chip architecture and the supporting software compiler toolchain required to deploy AI models on its hardware.\n\nThe company raised approximately $160 million in venture funding from investors including Intel Capital, Export Development Canada, and BDC Capital. Untether AI targets inference-intensive AI applications including edge AI deployment, data centre inference servers, and cloud inference acceleration where power efficiency and throughput per dollar are the primary evaluation metrics. The compute-near-memory approach is architecturally distinct from both GPU-based inference and other AI accelerator designs, with published benchmarks showing significant performance-per-watt advantages for specific inference workload types.\n\nUntether AI competes in the AI inference chip market against NVIDIA TensorRT, Groq, Cerebras, and other AI accelerator companies pursuing different architectural approaches to the inference efficiency problem. Canada position as a global AI research hub, anchored by the Vector Institute in Toronto and Mila in Montreal, provides a strong talent pipeline and research collaboration environment for deep chip design and AI systems work. The company architecture-first approach requires a longer path to production deployment than companies adapting existing GPU infrastructure, but targets a structurally different point on the performance-efficiency curve. 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 2 years of extra runway. Untether AI didn't arrive until 2018. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Untether AI has about 100-500 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 Untether AI and Graphcore compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.