Graphcore vs Neysa

Graphcore has a disclosed valuation of $2.8B, 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 $700M, compared to Neysa's $30M — a gap of $670M.

Graphcore holds the higher Awaira Score at 85/100, compared to Neysa's 55/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 30-point gap that reflects meaningful differences in scale or traction.

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

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 Colosseum 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. 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.

Graphcore was founded first in 2016, giving it 7 years of additional market experience. Neysa was founded later in 2023. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Graphcore has approximately 500-1000 employees, while Neysa has approximately 50-200. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Graphcore and Neysa are direct competitors — both operate in the AI Infrastructure space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.