Alternatives to Graphcore

AI chip startup focused on energy-efficient inference acceleration, acquired by AMD.

Designs high-performance AI processors and RISC-V based compute solutions.

TYLsemi is a chiplet-first silicon company focused on AI infrastructure, delivering production-ready chiplets for connectivity, power, and efficiency to accelerate custom AI accelerator design.

Industry-leading AI infrastructure for ultra-fast AI inference and training powered by Wafer-Scale Engines.

Develops AI chips for edge computing, enabling AI processing on devices.

Develops AI and Deep Learning processors for data centers and cloud environments.

Rethinking compute architectures for future processing needs with novel VLSI designs.

Semiconductor hardware company accelerating data analytics query processing speed and scale.

Develops the C200 Analytics Processing Unit (APU), an ASIC for massive-scale analytics.

Semiconductor startup designing AI-as-a-Service infrastructure for cloud, edge, and fog.

Leading semiconductor manufacturer designing and producing microprocessors for computing devices.

Marvell Technology is a global leader in data infrastructure semiconductor solutions, enabling the movement, processing, storing, and securing of the world's data.

Neuromorphic AI computing hardware designed for ultra-efficient, brain-inspired computation.

Fabless semiconductor company designing AI vision processors for edge devices.

Designs and manufactures high-performance microprocessors and graphics processors for computing and data center markets.

Provides high-performance computing infrastructure for AI/ML workloads.

Develops analog compute-in-memory solutions for AI inference acceleration, focusing on energy efficiency and performance.

Developing the Omni Processing Unit (OPU) that unifies CPU, GPU, memory, and connectivity for AI workloads.

Global technology company renowned for its GPUs, SoCs, and APIs, driving advancements in AI, gaming, and high-performance computing.

Neurophos develops photonic AI chips (OPUs) for extreme efficiency and performance in AI compute.