Monitor every NVIDIA GPU in your data center with real-time utilization, memory, temperature, and CUDA performance metrics. Purpose-built for AI clusters, model training, and high-performance computing — with all data stored on-premise.
Track the utilization percentage of each GPU in real time with one-second granularity. Identify idle GPUs, detect compute bottlenecks, and optimize resource allocation across your training and inference clusters.
Monitor allocated, used, and available video memory on every GPU. Receive alerts before out-of-memory (OOM) errors occur and optimize batch sizes and model partitioning to maximize the 80 GB HBM3 across your A100 and H100 cards.
Analyze CUDA and Tensor Core performance with detailed SM occupancy, operation throughput, and kernel efficiency metrics. Correlate hardware performance with the training progress of your AI models.
Track die temperature, fan speeds, and wattage consumption in real time. Configure preventive throttling policies and receive alerts when thermal conditions threaten hardware stability or the lifespan of your GPUs.
Manage hundreds of GPUs distributed across multiple nodes with a unified view. Monitor NVLink and NVSwitch interconnects, PCIe bandwidth, and inter-node communication to ensure optimal performance in large-scale distributed training.
Profile training and inference jobs with end-to-end metrics: time per epoch, sample throughput, per-pipeline-stage GPU usage, and data loader efficiency. Identify CPU, network, and GPU bottlenecks to accelerate your development cycles.
| Supported GPUs | NVIDIA A100, H100, H200, L40S, L4, T4, V100, RTX 4090/5090, and the full datacenter lineup |
| DCGM integration | Native integration with NVIDIA DCGM 3.x for driver-level metric collection with profiling field support |
| Metrics per GPU | 90+ metrics per GPU: utilization, memory, temperature, power, clocks, ECC, PCIe, NVLink, and more |
| Collection frequency | From 100 ms for critical metrics | 1s standard | Configurable per metric group |
| MIG support | Full Multi-Instance GPU (MIG): independent per-instance metrics on A100 and H100 with up to 7 partitions |
| NVML integration | Direct access via NVIDIA Management Library (NVML) for low-latency metrics without external agent dependencies |
| Orchestrator compatibility | Kubernetes (GPU Operator), Slurm, Docker with NVIDIA Container Toolkit, and bare-metal |
| Supported AI frameworks | PyTorch, TensorFlow, JAX, ONNX Runtime, TensorRT, and any CUDA 11.x/12.x-based workload |
Every minute of idle GPU time is money lost. With ByLoniS GPU NVIDIA, you gain complete visibility into your accelerators — from die temperature to the throughput of every CUDA kernel — all stored securely within your infrastructure.