Run:ai for the Rest: Cross-Vendor GPU Optimization
Run:ai optimizes NVIDIA clusters. But what about AMD, Intel, and mixed fleets? DeepLM is building the cross-vendor optimization layer for heterogeneous GPU infrastructure.
The Vendor Lock-in Problem
Run:ai built an excellent optimization platform — for NVIDIA-only clusters. But the GPU landscape is diversifying fast:
- AMD MI300X is gaining traction in training workloads
- Intel Gaudi is emerging for inference
- Cerebras and custom ASICs are entering production
- Multi-vendor fleets are becoming the norm, not the exception
Organizations running heterogeneous hardware need an optimization layer that works across all of it.
What "Cross-Vendor" Actually Means
It's not just supporting multiple GPU types. True cross-vendor optimization means:
- Workload profiling that understands which jobs perform best on which hardware
- Automatic migration — moving a training run from NVIDIA A100s to AMD MI300X without code changes
- Unified scheduling across CUDA, ROCm, and OneAPI from a single control plane
- Cost optimization that factors in hardware-specific pricing and availability
The DeepLM Approach
DeepLM's scheduler speaks Kubernetes and SLURM natively. It profiles workloads against available hardware, scores placement options, and routes jobs to the optimal GPU — regardless of vendor.
The telemetry layer captures per-operation GPU utilization, memory bandwidth, and thermal characteristics. This data feeds back into the scheduler, making every subsequent placement smarter.
Full Stack Support
| Layer | Technologies | |-------|-------------| | Orchestration | Kubernetes, SLURM | | GPU Compute | CUDA, ROCm, OneAPI | | Hardware | NVIDIA, AMD, Intel, Cerebras | | Monitoring | Prometheus, custom telemetry |
Try It
DeepLM Insights is free and open source. Start with observability, graduate to optimization.