This is a crazy oversimplification, but let me take a shot. The difference to others is easily a large "novel size" discussion.
Most training SoCs are focused on building something the size of an NVIDIA GPU, but designed for ML versus general purpose GPU HPC compute (FP64) plus ML. Often those accelerators today have a few types of models they are optimized for. NVIDIA is the baseline and so the competitors are looking for areas where they can get a large boost at a lower cost with something about the size of a A100/H100.
Cerebras is perhaps the biggest exception with its WSE-2, a wafer size chip. Having the wafer size chip means that Cerebras does not need to go into higher-latency and higher-power off-package interconnect as frequently because its chip is 50x larger. In turn, Cerebras drives performance and cost savings by not needing NVLink4 NVSwitches / InfiniBand.
Tesla's Dojo Tile is 25 chips roughly equivalent in size to a NVIDIA GPU in a single package with die-to-die communication facilitated by the base tile and then built for scale up units. Tesla also has focused on the interconnect and pipeline feeding the D1s and Tiles.
Ultimately, I think that it takes something beyond a "solution X saves 30% over NVIDIA in these workloads in performance/ $" to survive. NVIDIA has a massive software ecosystem and can handle more types of tasks versus some of the other AI accelerators. That goes beyond just the training and also to other parts of the data prep and movement pipeline. NVIDIA extracts high margins from this work so that is why some effectively are competing with "it costs less and on some problems can be faster" architectures but what Tesla, Cerebras, Google, and a few others have another level of differentiation.
Nothing is perfect, nor was that explanation, but just a high-level view of why the technology featured is impactful.
Most training SoCs are focused on building something the size of an NVIDIA GPU, but designed for ML versus general purpose GPU HPC compute (FP64) plus ML. Often those accelerators today have a few types of models they are optimized for. NVIDIA is the baseline and so the competitors are looking for areas where they can get a large boost at a lower cost with something about the size of a A100/H100.
Cerebras is perhaps the biggest exception with its WSE-2, a wafer size chip. Having the wafer size chip means that Cerebras does not need to go into higher-latency and higher-power off-package interconnect as frequently because its chip is 50x larger. In turn, Cerebras drives performance and cost savings by not needing NVLink4 NVSwitches / InfiniBand.
Tesla's Dojo Tile is 25 chips roughly equivalent in size to a NVIDIA GPU in a single package with die-to-die communication facilitated by the base tile and then built for scale up units. Tesla also has focused on the interconnect and pipeline feeding the D1s and Tiles.
Ultimately, I think that it takes something beyond a "solution X saves 30% over NVIDIA in these workloads in performance/ $" to survive. NVIDIA has a massive software ecosystem and can handle more types of tasks versus some of the other AI accelerators. That goes beyond just the training and also to other parts of the data prep and movement pipeline. NVIDIA extracts high margins from this work so that is why some effectively are competing with "it costs less and on some problems can be faster" architectures but what Tesla, Cerebras, Google, and a few others have another level of differentiation.
Nothing is perfect, nor was that explanation, but just a high-level view of why the technology featured is impactful.