Prosessori AMD EPYC 9355 3,55 GHz 256 MB L3 kanta SP5

Processor AMD EPYC 9355 3.55 GHz with 256 MB L3 cache is a server-class CPU based on the 5th Gen AMD EPYC family. Designed for data center, cloud and AI workloads, the EPYC 9355 uses the SP5 socket and supports dodeca-channel DDR5 memory for high memory bandwidth and capacity, enabling efficient handling of compute-intensive and multi-threaded server tasks.

Advantages

The AMD EPYC 9355 offers high core density (32 cores) and a large 256 MB L3 cache to improve throughput on server workloads. With a base frequency of 3.55 GHz and boost up to 4.4 GHz, it delivers strong single-thread and multi-thread performance. Support for dodeca-channel DDR5 memory provides up to 576 GB/s memory bandwidth, which helps reduce bottlenecks in memory-bound applications. The EPYC 9355 is built for energy-efficient, high-density deployments typical for cloud and AI inference use cases.

Suitable for

• Virtualization and cloud infrastructure (vCPU-dense hosts)
• AI inference on CPU-only servers and hybrid CPU+GPU systems
• High-performance computing and data analytics
• Database servers and large-scale transaction processing

Key specifications

• Processor family: AMD EPYC
• Model: 9355
• Socket: SP5
• Base frequency: 3.55 GHz
• Boost frequency: up to 4.4 GHz
• Cores: 32
• L3 cache: 256 MB
• Memory: dodeca-channel DDR5-SDRAM
• Maximum memory bandwidth (platform): up to 576 GB/s
• On-board graphics: not available

How to use

Install the EPYC 9355 into a compatible SP5 motherboard following standard server assembly procedures, ensuring proper socket handling and cooling. Configure the server BIOS/firmware to recognize DDR5 memory channels and set memory and power profiles according to workload requirements. Allocate cores and threads in your virtualization or orchestration platform to match application demands, and pair with appropriate storage and networking to avoid I/O bottlenecks.

Recommendations

Use this processor in well-cooled rack servers for cloud, virtualization, and inference workloads. For best efficiency, balance memory capacity and channel population to exploit the dodeca-channel DDR5 configuration and consider GPU acceleration where large model inference or training demands exceed CPU-only performance.