Machine Analysis of a High-Performance ROCm Server | Generated by AI
Here’s the complete machine analysis:
Machine Analysis: 129.212.178.209
Overview
| Property | Value |
|---|---|
| Hostname | 7 |
| OS | Ubuntu 24.04.4 LTS (Noble Numbat) |
| Kernel | 6.8.0-106-generic (x86_64) |
| Uptime | ~54 minutes (fresh) |
| Virtualization | KVM/QEMU (pc-q35-6.1) |
CPU — Intel Xeon Platinum 8568Y+ (virtualized)
| Property | Value |
|---|---|
| vCPUs | 20 cores, 1 socket, 1 thread/core |
| ISA | x86_64, AVX-512 (full: VL, DQ, BW, BF16, VNNI, VBMI, FP16, etc.) |
| L1 cache | 640 KB (32 KB × 20 instances) |
| L2 cache | 80 MB total (4 MB × 20) |
| BogoMIPS | 4600.00 |
| Hypervisor | KVM, full virtualization with VT-x |
This is a cloud VM slice of a real Intel Xeon — you get 20 dedicated cores with heavy AVX-512 support, which matters for token embedding throughput and prefill in LLM serving.
GPU — AMD Instinct MI300X VF (gfx942 / CDNA3)
| Property | Value |
|---|---|
| Model | AMD Instinct MI300X VF |
| Architecture | gfx942 (CDNA3) |
| Compute Units | 304 (32 SE × ~9.5 CU/SE) |
| SIMDs | 1,216 (4 per CU) |
| Max Clock | 2,100 MHz |
| VRAM | 191.7 GiB (205,822,885,888 bytes) |
| VRAM Used | ~286 MB (essentially idle) |
| GTT Memory | ~118 GiB available |
| L2 Cache | 4 MB |
| L3 Cache | 256 MB |
| PCIe | Gen5 x16 @ 32 GT/s |
| Compute Partition | SPX (single GPU) |
| Memory Partition | NPS1 (single partition, not NUMA-split) |
| Temperature | 43°C junction / 38°C memory |
| Power Draw | 157W idle |
| GPU Utilization | 0% (completely idle) |
| State | Low-power mode (auto-sleeping) |
This is the top-tier AMD datacenter GPU — 192 GB HBM3, ideal for large model inference (Llama-405B, DeepSeek-V3 at FP8). The low-power state just means no workload is running; it’ll ramp up to full clocks when loaded.
ROCm Software Stack
| Component | Version |
|---|---|
| ROCm | 7.2.0 (70200-43) |
| Driver | 6.16.13 |
| HIP SDK | 7.2.26015 (AMD clang 22.0.0) |
| rocm-smi-lib | 7.8.0 |
| ROCm LLVM | 22.0.0 |
| ROCgdb | 16.3 |
| MIOpen | ✓ installed |
| MIGraphX | ✓ installed |
| RCCL (NCCL equivalent) | ✓ installed |
| OpenCL | ✓ 2.0 |
| OpenMP | ✓ installed |
Full ROCm toolchain is installed — HIP compiler, debugger, ML libraries (MIOpen, MIGraphX, RCCL). You can compile HIP/C++ kernels or run PyTorch with ROCm backend.
Missing: No PyTorch or any Python ML packages installed (only PyYAML). You’ll need to install torch with ROCm support.
Memory & Storage
| Resource | Total | Used | Free |
|---|---|---|---|
| System RAM | 235 GiB | 5.7 GiB | 229 GiB |
| Swap | 0 (none configured) | — | — |
| OS Disk (vda1) | 719 GB | 87 GB | 610 GB free |
| Data Disk (vdc1) | 5 TB | — | unmounted |
Key observation: the 5TB data disk is formatted (ext4) but not mounted. You’ll want to mount it — great for model weights, datasets, or Docker volumes.
Docker
| Property | Value |
|---|---|
| Docker | 29.3.0 + Compose v5.1.0 |
| Running container | rocm — port 8888 (likely Jupyter) |
| GPU access | ✓ confirmed (/dev/kfd, /dev/dri accessible) |
Network
| Interface | IP | Purpose |
|---|---|---|
| eth0 | 129.212.178.209/20 | Public / AMD DevCloud |
| eth1 | 10.128.0.2/20 | Private/internal |
| docker0 | 172.17.0.1/16 | Docker bridge |
Overall Assessment
This is an AMD DevCloud ATL1 instance — a cloud-allocated MI300X node for development. It’s freshly booted, completely idle, with a massive 192 GB GPU ready for large-model work.
Immediate recommendations:
- Mount the 5TB disk —
mount /dev/vdc1 /dataor similar, then add to fstab - Install PyTorch for ROCm —
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2 - Wake the GPU — the low-power state is normal; it auto-wakes on workload
- Consider switching memory partition to NPS4 if you need NUMA-aware multi-process access (currently NPS1, which treats the GPU as one monolithic memory pool — fine for single-process inference)
Want me to mount the 5TB disk and install PyTorch?