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AI/DL Workloadoptimization.

GPU clusters waste 60% of capacity. We fix that. Intelligent scheduling across every vendor.

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Building partnerships with
NVIDIA
AMD
Intel
ARM
SuperMicro
Cerebras
NVIDIA
AMD
Intel
ARM
SuperMicro
Cerebras
NVIDIA
AMD
Intel
ARM
SuperMicro
Cerebras
NVIDIA
AMD
Intel
ARM
SuperMicro
Cerebras

The GPU crisis is real.

Billions spent on GPU infrastructure. Most of it wasted.

60%

Wasted GPU Capacity

GPU clusters run at just 40% utilization. Billions in compute sitting idle.

Training Runs Too Long

Poor scheduling and resource contention make every job take 4× longer.

10×

Demand Outstrips Supply

GPU demand is 10× greater than supply. Every wasted cycle is costly.

0

No Cross-Vendor Solution

No tool optimizes across vendors, families, and generations.

The fix is DeepLM.

One platform. Every GPU. Full optimization.

Intelligent Scheduling

AI-driven resource allocation across compute, storage, and network layers. DeepLM assigns the right GPU to the right workload at the right time — eliminating queue wait and resource contention.

Cross-Hardware Support

Seamless optimization across NVIDIA, AMD, and Intel GPUs. Move workloads freely between vendors and generations without code changes or manual reconfiguration.

Continuous Telemetry

Real-time telemetry feeds a continuously learning optimization engine. Every workload, every scheduling decision, every thermal reading makes the system smarter at eliminating bottlenecks.

85%+ Fleet Utilization

Baseline your current GPU occupancy, then watch DeepLM push it from the industry average of 40% to 85%+ across your entire fleet. Measure the savings in real dollars.

Cloud-grade GPU infra.

Run:ai for the rest. Full-stack observability and optimization across Kubernetes, SLURM, ROCm, and CUDA — regardless of vendor or generation. Monitor utilization, thermals, power, and workload health at every layer.

KubernetesROCmCUDASLURMNVIDIAAMD
GPU-0
87%
UTIL
72°
TEMP
285W
PWR
GPU-1
NIC-0
CHASSIS
DeepLM

From insights to optimizer.

DeepLM ships in two phases. The first release gives your team full visibility into GPU fleet performance — utilization, scheduling bottlenecks, and node health. The second release builds on that telemetry to automatically migrate workloads across vendors and generations, turning observability into optimization.

Free

DeepLM Insights

Real-time observability and intelligent scheduling.

GPU observability dashboards
Workload scoring & prioritization
Intelligent scheduling engine
Node health monitoring
Paid Upgrade

DeepLM Optimizer

Cross-vendor workload migration and optimization.

Multi-instance GPU management
Cross-vendor workload migration
NVIDIA → AMD seamless
Inference optimization

Infrastructure veterans.

Our founding team comes from the companies that built modern AI and cloud infrastructure.

Founders & Engineers

Google
NVIDIA
Microsoft Research
Apple
DeepMind

Advisors

MIT
Bell Labs
Lucent
Microsoft
HashiCorp

Start building.

Fork the repo, spin up a cluster, and see the difference.

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