Control Your GPU Infra with 0 Code Changes
Deploy and manage AI workloads with intelligent queuing and real-time monitoring to maximize your GPU cluster performance.
The fastest, most seamless GPU cloud setup we've experienced after using Slurm, Azure, GCP, Mosaic, Foundry, Together, and Lambda.
Run your cluster from Claude Code
Konduktor exposes your jobs over the Model Context Protocol, so Claude can launch workloads, read logs, and pull GPU metrics with your approval. Connect once, then operate the cluster in plain language.
● Reading logs for train-7b
└ Rank 3 hit a CUDA OOM at step 8400
● Checking GPU metrics on the node
└ Memory at 99% right before the crash
● Restarting train-7b on healthy GPUs
└ Back in the queue
» auto mode on
Connect Konduktor over the Model Context Protocol with one command, then ask in plain language.
Launch jobs
Submit a YAML workload to the queue without leaving your editor.
Debug from logs
Read job and cluster logs to find why a run failed, then restart it.
Diagnose from metrics
Pull in-depth GPU and system metrics to trace a slowdown to its cause.
Meet Konduktor
Deploy AI workloads at scale on any cloud with a simple YAML file. Paste in your existing torchrun command, set num_nodes, and Konduktor handles the distributed setup.
Catch hardware failures before they kill your run
Konduktor detects bad GPUs and nodes, cordons them, and reschedules your job onto healthy hardware automatically.
Take control and get visibility across your AI infrastructure
Get real-time visibility into GPU usage and costs to make smarter infrastructure decisions.
Scale to multi-node training on any cloud.
Run distributed training across nodes on high-bandwidth interconnects like InfiniBand and RoCE, with the networking set up for you. Train on your reserved GPUs in any cloud.
Preemptive Queue
Train ML workloads with priority queuing. High-priority jobs pause lower ones and resume them on completion.
Fault-Tolerant Infrastructure
Zero disruption with built-in failover. Monitor your workloads with real-time dashboards.
Health Monitoring
Continuous health checks, fault detection, and recovery keep your training jobs running on healthy GPUs.
Maximize GPU Cluster Performance at Enterprise Scale
Scale your training beyond thousands of GPUs while maintaining precise control over resource allocation, priority queuing, and cluster performance.
Operate jobs with Claude
Launch and restart jobs, read cluster and job logs, and pull GPU metrics with Claude through Konduktor's MCP integration.
Performance Metrics
Monitor your workloads with real-time dashboards and advanced utilization tracking to optimize your GPU usage.
Resource Management
Take control of your GPU resources with comprehensive utilization tracking and allocation tools.
Performance Verified
We stress-test every metric your cloud provider promises and help with real-time resolution.