Explore Massed Compute's A100_80G cloud instance specifications and benchmarks. Compare hardware configurations and performance metrics to optimize your AI and ML workloads.
Hardware Specifications
| GPU Configuration | Value |
|---|---|
| GPU Type | A100_80G |
| GPU Interconnect | PCIE |
| GPU Model Name | NVIDIA A100 80GB PCIe |
| Driver Version | 560.35.03 |
| GPU VRAM | 160 GB |
| Power Limit (W) | 300.00 |
| GPU Temperature (°C) | 44 |
| GPU Clock Speed (MHz) | 210 |
| Memory Clock Speed (MHz) | 1512 |
| Pstate | P0 |
| CPU Configuration | Value |
|---|---|
| Model Name | AMD EPYC 7502 32-Core Processor |
| Vendor ID | AuthenticAMD |
| CPUs | 30 |
| CPU Clock Speed | 4999.99 |
| Threads Per Core | 1 |
| Cores Per Socket | 15 |
| Sockets | 2 |
| Memory | Value |
|---|---|
| Total | 147Gb |
Disks Specifications
| Storage | Value |
|---|---|
| Total | 1536.00 GB |
Available Disks
| Property | Value |
|---|---|
| Disk 1 | |
| Model | sda |
| Size | 1.5Tb |
| Type | HDD |
| Mount Point | Unmounted |
Software Specifications
| Software | Value |
|---|---|
| OS | Ubuntu |
| OS Version | 22.04.4 LTS (Jammy Jellyfish) |
| Cuda Driver | 12.6 |
| Docker Version | 27.2.0 |
| Python Version | Python 3.10.12 |
Benchmarks
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| Benchmark | Value |
|---|---|
| ffmpeg | 222ms |
| Coremark (Iterations per sec) | 23607.177 |
| llama2Inference (Tokens per sec) | 38.51 |
| Tensorflow Mnist Training | 2.112 |