NVIDIA introduces Minerva, a solution that turns telemetry into GPU productivity. The technology converts telemetry data into performance optimization for GPUs in complex AI environments. Minerva is based on CUDA Tile, a domain-specific language in Python.
Minerva is based on CUDA Tile, a domain-specific language in Python that automates hardware usage to optimize GPU programming at the tile level. NVIDIA also removed the RTX 5090 from its marketplace, and Micro Center listed it as 'not carried'. The RTX 5080 becomes the company's new flagship, with prices that are already high.
Modders have unlocked NVIDIA's limitation on GeForce GPUs, increasing KI processing capacity. The RTX 4090 reaches 331.6 TFLOPS in FP16 and BF16, while the RTX 3090 Ti achieves 157.4 TFLOPS in BF16. The modders' modifications work without altering the VBIOS or drivers, but they are limited to Linux and may cause boot problems.
The improvement from the modifications is more significant in work tasks, such as KI, than in gaming. The modifications are only available for RTX 20, 30 and 40 GPUs, and there is no support for the RTX 50. NVIDIA is focusing on improving GPU productivity in complex AI environments with solutions such as Minerva and CUDA Tile. At the same time, the company is reorienting its strategy toward AI GPUs, reducing the availability of gaming models, such as the RTX 5090, while modders are unlocking limitations on earlier GPUs to boost performance in KI tasks.
For tasks that demand high precision, BF16 performance also reaches 331.6 TFLOPS, maintaining efficiency even when using low-precision floating-point numbers. The RTX 3090 Ti, in turn, delivers 157.4 TFLOPS in BF16, a significantly lower figure, but still useful for applications that do not require maximum performance. NVIDIA is redefining GPU productivity in complex AI environments with Minerva, which turns telemetry into performance optimization. The company has already explored technologies such as Kumo and CUDA-X, which aim to improve data analysis and simulations. This approach lets developers write kernels in Python, while compiler optimizations manage thread and warp scheduling.
The technology may limit applications to specific parallel processing tasks, but it offers greater flexibility in complex AI environments. NVIDIA has not disclosed pricing or availability in Brazil, but Minerva is aimed at professionals who work with parallel processing on GPUs in complex AI environments. NVIDIA Minerva and CUDA Tile redefine how GPUs handle telemetry and parallel processing, optimizing productivity in complex AI environments. The technology turns telemetry data into insights that improve GPU productivity, enabling greater efficiency in AI environments. Modders have unlocked limitations in GeForce GPUs, increasing AI processing capacity without changing drivers or VBIOS, with a greater impact on work tasks.
With information from developer.nvidia.com, techradar.com and computerbase.de.
Sources
- How nvidia minerva turns telemetry chaos into peak gpu yielddeveloper.nvidia.com
- Accelerating mamba 2 with high performance forward pass kernels in nvidia cuda tiledeveloper.nvidia.com
- Are we seeing the first signs of nvidia abandoning pc gamers as the rtx 5090 seemingly disappears from shelves im getting more worried about geforce gpustechradar.com
- Geforce rtx 20 30 und 40 modder loesen nvidias bremse fuer ki berechnungencomputerbase.de
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