GPUs can now use PCIe-attached memory or SSDs to boost VRAM capacity
Major companies like AMD, Intel, and Nvidia are considering supporting Panmnesia's CXL IP technology for GPU memory expansion using PCIe-attached memory or SSDs. The low-latency CXL IP addresses increasing memory needs for AI training datasets, offering improved GPU performance for AI and HPC applications. Adoption by GPU manufacturers is uncertain, potentially shaping future GPU memory expansion technologies.
Read original article
Companies like AMD, Intel, and Nvidia may soon support Panmnesia's CXL IP technology, enabling GPUs to expand memory capacity using PCIe-attached memory or SSDs. Panmnesia's low-latency CXL IP allows for memory expansion beyond the built-in high-bandwidth memory in GPUs, addressing the increasing memory requirements for AI training datasets. The technology involves a CXL 3.1-compliant root complex and host bridge to connect external memory over PCIe to the GPU's system bus, mimicking system memory behavior. Testing shows significantly reduced latency compared to existing prototypes, enhancing GPU performance for AI and HPC applications. While CXL support offers potential benefits, its adoption by major GPU manufacturers like AMD and Nvidia remains uncertain. Whether these companies will integrate CXL support or develop proprietary solutions in response to the trend of using PCIe-attached memory for GPUs remains to be seen, shaping the future landscape of GPU memory expansion technologies.
Related
First 128TB SSDs will launch in the coming months
Phison's Pascari brand plans to release 128TB SSDs, competing with Samsung, Solidigm, and Kioxia. These SSDs target high-performance computing, AI, and data centers, with larger models expected soon. The X200 PCIe Gen5 Enterprise SSDs with CoXProcessor CPU architecture aim to meet the rising demand for storage solutions amidst increasing data volumes and generative AI integration, addressing businesses' data management challenges effectively.
Testing AMD's Giant MI300X
AMD introduces Radeon Instinct MI300X to challenge NVIDIA in GPU compute market. MI300X features chiplet setup, Infinity Cache, CDNA 3 architecture, competitive performance against NVIDIA's H100, and excels in local memory bandwidth tests.
AMD MI300X performance compared with Nvidia H100
The AMD MI300X AI GPU outperforms Nvidia's H100 in cache, latency, and inference benchmarks. It excels in caching performance, compute throughput, but AI inference performance varies. Real-world performance and ecosystem support are essential.
Extreme Measures Needed to Scale Chips
The July 2024 IEEE Spectrum issue discusses scaling compute power for AI, exploring solutions like EUV lithography, linear accelerators, and chip stacking. Industry innovates to overcome challenges and inspire talent.
GPUs can now use PCIe-attached memory or SSDs to boost VRAM capacity
Major companies like AMD, Intel, and Nvidia are considering supporting Panmnesia's CXL IP for GPU memory expansion using PCIe-attached memory or SSDs. Panmnesia's low-latency solution outperforms traditional methods, showing promise for AI/HPC applications. Adoption by key players remains uncertain.
4 commentsRelated
First 128TB SSDs will launch in the coming months
Phison's Pascari brand plans to release 128TB SSDs, competing with Samsung, Solidigm, and Kioxia. These SSDs target high-performance computing, AI, and data centers, with larger models expected soon. The X200 PCIe Gen5 Enterprise SSDs with CoXProcessor CPU architecture aim to meet the rising demand for storage solutions amidst increasing data volumes and generative AI integration, addressing businesses' data management challenges effectively.
Testing AMD's Giant MI300X
AMD introduces Radeon Instinct MI300X to challenge NVIDIA in GPU compute market. MI300X features chiplet setup, Infinity Cache, CDNA 3 architecture, competitive performance against NVIDIA's H100, and excels in local memory bandwidth tests.
AMD MI300X performance compared with Nvidia H100
The AMD MI300X AI GPU outperforms Nvidia's H100 in cache, latency, and inference benchmarks. It excels in caching performance, compute throughput, but AI inference performance varies. Real-world performance and ecosystem support are essential.
Extreme Measures Needed to Scale Chips
The July 2024 IEEE Spectrum issue discusses scaling compute power for AI, exploring solutions like EUV lithography, linear accelerators, and chip stacking. Industry innovates to overcome challenges and inspire talent.
GPUs can now use PCIe-attached memory or SSDs to boost VRAM capacity
Major companies like AMD, Intel, and Nvidia are considering supporting Panmnesia's CXL IP for GPU memory expansion using PCIe-attached memory or SSDs. Panmnesia's low-latency solution outperforms traditional methods, showing promise for AI/HPC applications. Adoption by key players remains uncertain.