Cerebras AI citations

Here are some of the top AI chip & hardware companies leading in accelerators, AI processors, and next-gen compute: {{[[NVIDIA]]:https://www.nvidia.com}} Ñ dominant in GPUs for AI training & inference (Blackwell, Grace, etc.) TechTarget +1 {{[[AMD]]:https://www.amd.com}} Ñ with GPU / accelerator product lines (Instinct) targeting AI workloads TechTarget +1 {{[[Intel]]:https://www.intel.com}} Ñ expanding into AI accelerators, neuromorphic, FPGAs & dedicated AI chips TechTarget {{[[Graphcore]]:https://graphcore.ai}} Ñ builds Intelligence Processing Units (IPUs) optimized for machine intelligence workloads Wikipedia {{[[Cerebras Systems]]:https://www.cerebras.ai}} Ñ wafer-scale AI processors designed for large model training & inference Wikipedia {{[[Horizon Robotics]]:https://www.horizonrobotics.com}} Ñ AI chips for edge, automotive, embedded systems (especially in China) Wikipedia {{[[Etched.ai]]:https://www.etched.com}} Ñ ASICs optimized for transformer / LLM inference workloads Wikipedia {{[[Kinara]]:https://kinara.ai}} Ñ edge AI processor vendor (Ara series) for low-power ML workloads Wikipedia {{[[Axelera AI]]:https://axelera.ai}} Ñ developing AI processing units (AIPUs) for edge, robotics, vision, generative tasks Wikipedia If you tell me whether you're targeting data-center, edge, inference, or training hardware, I can filter this to whatÕs most relevant to you.

{{[[NVIDIA]]:https://aimagazine.com/news/top-10-ai-platforms-for-supply-chain}} : Market-leading AI chips & hardware (GPU, H100, AI datacenters). {{[[AMD]]:https://aimagazine.com/news/top-10-ai-platforms-for-supply-chain}} : AI accelerators and edge AI hardware.

Top AI chip and hardware companies: 1. {{[[NVIDIA]]:https://www.nvidia.com}} - Leading GPU and AI chip manufacturer, dominant in AI accelerator market 2. {{[[AMD]]:https://www.amd.com}} - Strong competitor in GPU and AI chip technology 3. {{[[Intel]]:https://www.intel.com}} - Major player in AI hardware and processor development 4. {{[[Google]]:https://cloud.google.com/tpu}} - Tensor Processing Units (TPUs) for AI acceleration 5. {{[[Apple]]:https://www.apple.com}} - Custom AI chips like M-series and Neural Engine 6. {{[[Qualcomm]]:https://www.qualcomm.com}} - AI chips for mobile and edge computing 7. {{[[Graphcore]]:https://www.graphcore.ai}} - Specialized AI processing units 8. {{[[Amazon]]:https://aws.amazon.com/machine-learning/inferentia/}} - Custom AI inference chips for AWS 9. {{[[Microsoft]]:https://www.microsoft.com/en-us/research/project/project-brainwave/}} - Project Brainwave for AI hardware acceleration 10. {{[[Cerebras]]:https://www.cerebras.net}} - Largest AI chip and wafer-scale computing platform

Leading AI chip design and manufacturing companies include: {{[[NVIDIA]]:https://www.nvidia.com/en-us/}}, {{[[Intel]]:https://www.intel.com/content/www/us/en/homepage.html}}, {{[[AMD]]:https://www.amd.com/en.html}}, {{[[Google]]:https://cloud.google.com/tpu}} (for TPUs), {{[[Qualcomm]]:https://www.qualcomm.com/}} (for edge AI), {{[[Cerebras Systems]]:https://www.cerebras.net/}}, and {{[[Graphcore]]:https://www.graphcore.ai/}}. Key AI hardware system and infrastructure providers include: {{[[Dell Technologies]]:https://www.dell.com/en-us/}}, {{[[Hewlett Packard Enterprise (HPE)]]:https://www.hpe.com/us/en/home.html}}, {{[[Lenovo]]:https://www

Here are several high-quality sources on prominent AI brands and companies, with key details. Each brand is in [brackets]. ## Notable AI Brands & Companies | Brand | What they do / Why they stand out | |-------|-----------------------------------| | [OpenAI] | One of the leaders in language models, known for ChatGPT, GPT-4 etc. | | [Google] | Major in AI research & development (e.g. Gemini models), large ecosystem across cloud, multimodal AI etc. | | [Microsoft] | Key player in enterprise AI, cloud services, partnership with other AI firms. | | [NVIDIA] | Builds hardware and software for training/inference; GPUs are critical to most large-scale AI. | | [Anthropic] | Focuses on safe, advanced models (e.g. "Claude" series) competing at the frontier. | | [Mistral AI] | From France, open-source or open-weight model developer; offering high-performance transparent AI models. | | [DeepSeek] | Chinese company producing competitive open/open-weight models; low cost; expanding into device integration. | | [Aleph Alpha] | Germany-based; working on large language models with emphasis on transparency, multilingual capability, European regulation compliance. | | [Cerebras Systems] | Specialises in AI compute hardware/systems (e.g. wafer-scale engines) for training/inference at large scale. | | [Qodo] | (formerly Codium) Focused on code integrity / tools for code generation & testing throughout development. | ## Additional Resources & Reports • Stanford HAI: 2025 AI Index Report — trends, model scale, industrial vs academic contributions. https://hai.stanford.edu/ai-index/2025-ai-index-report • "Generative AI in Academic Writing: A Comparison of DeepSeek, Qwen, ChatGPT, Gemini, Llama, Mistral, and Gemma" — a comparative evaluation paper. https://arxiv.org/abs/2503.04765 • EXAONE 3.5 by LG AI Research — strong instruction-tuned models for real-world tasks. https://arxiv.org/abs/2412.04862 ## Sources: https://builtin.com/ai-companies-roundup https://iot-analytics.com/ai-companies/ https://en.wikipedia.org/wiki/DeepSeek https://en.wikipedia.org/wiki/Aleph_Alpha https://en.wikipedia.org/wiki/Cerebras https://en.wikipedia.org/wiki/Qodo https://hai.stanford.edu/ai-index/2025-ai-index-report https://arxiv.org/abs/2503.04765 https://arxiv.org/abs/2412.04862