- The iPad I recommend to most users is only $299 right now
- One of the most versatile action cameras I've tested isn't from GoPro - and it's on sale
- Small Manufacturers, Big Target: The Growing Cyber Threat and How to Defend Against It
- Why I pick this JBL speaker over competing models for outdoor listening
- Testing a smart cooler proved I can never go back to toting ice (and it's on sale)
AMD steps up AI competition with Instinct MI350 chips, rack-scale platform

Other announcements included ROCm 7, the latest version of AMD’s open-source AI software stack, and the broad availability of its Developer Cloud, a fully managed platform aimed at accelerating high-performance AI development.
Openness and Nvidia challenge
AMD underscored its commitment to open standards and ecosystem collaboration, positioning itself in contrast to rival Nvidia, which depends heavily on a proprietary software stack.
“We are entering the next phase of AI, driven by open standards, shared innovation, and AMD’s expanding leadership across a broad ecosystem of hardware and software partners who are collaborating to define the future of AI,” AMD’s chair and CEO, Lisa Su, said in the statement.
The announcement follows AMD’s recent acquisition of AI software startup Brium, a deal the company said brought in deep expertise to accelerate the open-source tools that power its AI software stack.
“When you look at the specs, AMD’s MI355X is taped out on TSMC’s N3P process, while Nvidia’s GB300 uses 4NP,” said Neil Shah, vice president for research and partner at Counterpoint Research. “This gives AMD a process node advantage in performance and power efficiency, especially compared to the Nvidia Blackwell GB200/B200.”
However, factors such as memory bandwidth, precision optimization, and software framework support ultimately determine training and inference performance.
“AMD excels at optimizations for higher precisions (FP64, FP32) where it holds an advantage, but Nvidia is better optimized for lower precisions (FP4, FP8),” Shah added. “AMD now matches Nvidia on core capabilities, allowing it to compete head-on, which could lead to improved theoretical TCO.”
However, AMD’s ROCm software stack is still catching up to Nvidia’s more established CUDA ecosystem, which remains a key factor in real-world performance and total cost of ownership. Broader adoption will depend on how quickly AMD can narrow this gap, though it continues to make steady progress with each generation.