Machine Analysis of a High-Performance ROCm Server | Generated by AI

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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:

  1. Mount the 5TB disk — mount /dev/vdc1 /data or similar, then add to fstab
  2. Install PyTorch for ROCm — pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2
  3. Wake the GPU — the low-power state is normal; it auto-wakes on workload
  4. 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?

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