AMD puts MI350 into production, readies Helios for 2026

AMD has put its Instinct MI350 accelerator family into production and previewed Helios, a rack-scale computing system that will combine its future MI400 GPUs with in-house processors and networking. The announcement, made Thursday at its Advancing AI event, underscores the company’s push to challenge Nvidia for a share of the AI infrastructure market.

The immediate news is the MI350X and MI355X, which AMD says are available through its server and cloud partners. AMD says the new generation delivers up to three times the AI computing capacity of the MI300 series and significantly improves inference performance—the stage at which a trained model responds to real user requests.

More memory for models that don’t fit on a single GPU

AMD’s central argument is not just computing power. Each MI350 comes with up to 288 GB of HBM3E memory, a high-speed memory technology integrated alongside the graphics processor. That figure matters because large language models, long context windows and applications serving many users need to store large amounts of data close to the GPU.

When a model does not fit on a single GPU, it has to be split across several GPUs, which must constantly coordinate the exchange of information. That operation adds cost, power consumption and latency. More memory per accelerator can simplify deployment, especially for companies running open-source models or their own systems rather than relying solely on an external API.

The MI350 chips are based on AMD’s CDNA 4 architecture and target both training and inference. Inference has become the industry’s most important battleground: training a large model is expensive and occasional, while serving millions of queries every day creates a permanent load for data centers.

Helios takes the competition from chips to the full rack

AMD also showed off Helios, its proposal for 2026. It will not be a standalone GPU but a rack architecture bringing together Instinct MI400 accelerators, next-generation EPYC processors codenamed Venice, and Pensando networking technology.

The move reflects how major AI companies have changed the way they buy infrastructure. Customers no longer compare one chip with another; they buy entire racks containing dozens of accelerators, high-speed interconnects, storage, cooling and software. Nvidia has turned that integrated approach into an advantage with its DGX systems and Blackwell racks. Helios is AMD’s answer to that model.

The company also says the design will be built around open standards. That promise matters to data center operators that want to combine components from different vendors and avoid making their entire infrastructure dependent on a single manufacturer. But it also presents a challenge: openness must translate into systems that are easy to install, maintain and scale—not just compatibility on paper.

ROCm 7, the piece that will determine adoption

Hardware alone is not enough to displace an established platform. Nvidia maintains its dominant position in part because of CUDA, its programming ecosystem and libraries for accelerating AI workloads. AMD needs its ROCm alternative to prove reliable for researchers, developers and operations teams.

That is why the company introduced ROCm 7, the next version of its open software stack. AMD promises performance improvements and broader compatibility with leading AI frameworks and models. The practical question will be how much work it takes to move projects built for CUDA to servers with Instinct chips, and whether its debugging, monitoring and optimization tools reach the maturity demanded by large-scale deployments.

The presence of Sam Altman, OpenAI’s CEO, alongside Lisa Su at the event reflects the importance of expanding the computing capacity available to the leading AI labs.

The MI350 chips allow AMD to compete now in the current generation of AI servers. Helios and the MI400 series will determine whether it can also compete for the infrastructure contracts to be decided for 2026, when a chip’s performance will be only one part of the decision.