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| Product Overview |
|---|
The AMD EPYC Genoa ES2 96-Core 1.20GHz Processor is a high-core-count server CPU designed to handle complex, multi-threaded workloads. It fits well into data center and enterprise server roles where efficient, scalable processing is essential for handling large volumes of parallel tasks. |
| General Information | |
|---|---|
| Brand | AMD |
| Part Number | 100-000000475-14-L |
| Technical Information | |
|---|---|
| Processor Family | EPYC Genoa |
| Total Threads | 192 |
| Base Clock Speed | 1.20 GHz |
| PCIe Version | PCIe 5.0 |
| Lithography | 5 nm |
| Socket Type | SP5 |
| Cache | L2: 96 MB, L3: 768 MB |
| Memory Specifications | |
|---|---|
| Max Memory Size | 6 TB |
| Memory Types | DDR5 |
| Max Memory Channels | 12 |
| Bandwidth | 460.8 GB/s |
| Physical Characteristics | |
|---|---|
| Weight | 3.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Eco Friendly | Yes |
| Product Description |
|---|
This AMD EPYC Genoa ES2 processor delivers robust performance across a vast number of cores, making it ideal for demanding server environments. Commonly found in data centers, it powers applications requiring extensive parallel processing, such as cloud computing and large-scale virtualization. Built for IT professionals and system architects, this processor supports workloads that benefit from high core counts and efficient energy use. It is often integrated into enterprise servers and high-performance computing clusters. Key Features
This processor is typically deployed in environments where high core counts and reliable processing power are critical, including cloud service providers and research institutions. Its ability to manage extensive parallel workloads helps maintain performance and stability in busy server farms. |
| Use Cases |
|---|
The AMD EPYC Genoa ES2 96-Core Processor is engineered to meet the needs of high-performance computing and enterprise server applications. How It's Used:
Its extensive core count and architecture make it a versatile choice for organizations aiming to enhance computational efficiency and scalability. |