Neysa vs Graphcore

Graphcore has a disclosed valuation of $600M, while Neysa'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 Neysa's $30M — a gap of $737M. Combined, the two companies have completed 5 known funding rounds.

Graphcore leads with an Awaira Score of 85/100, while Neysa sits at 55/100. That 30-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Neysa was founded by Shashank Samala in 2023. Graphcore was founded by Nigel Toon in 2016. Visit each company's profile on Awaira for a full founder biography.

Neysa: Neysa builds cloud infrastructure purpose-built for AI workloads, offering GPU-accelerated compute, storage, and networking optimized for training and inference at scale. The platform targets enterprises and AI labs in India that need high-performance compute without the cost and complexity of hyperscaler lock-in.\n\nThe company raised approximately $30M in Series A funding and has attracted early adopters across the Indian AI startup ecosystem seeking affordable, low-latency GPU access. Neysa operates its own data centers with an emphasis on cost-per-token efficiency for large model training.\n\nAs Indian AI labs and enterprises scale their model development ambitions, Neysa sits at the center of a critical infrastructure gap. Domestic GPU cloud capacity is severely constrained relative to demand, and Neysa is one of the few India-headquartered players building the physical and software stack to address it. Neysa competes in a rapidly growing Indian cloud GPU market alongside Yotta Data Services, E2E Networks, and global hyperscalers like AWS and Azure. The company differentiates by offering infrastructure specifically architected for AI workflows rather than retrofitted general-purpose cloud compute. With India's AI infrastructure spend projected to exceed $4 billion by 2027, Neysa targets enterprises and research institutions that need sovereign AI compute capacity without dependence on foreign cloud providers. The company's positioning aligns with the Indian government's push for domestic AI infrastructure through initiatives like the IndiaAI Mission. 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 7 years of extra runway. Neysa didn't arrive until 2023. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Neysa has about 50-200 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 Neysa and Graphcore compete in AI Infrastructure, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.