Your AIOps Teammate toScale Infra.
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Workload Explorer
Advanced search and filtering across all workloads
| Name | Status | Class | Project | User | GPU | Count | Submitted | Cost | |
|---|---|---|---|---|---|---|---|---|---|
| llama-ft-v2 | RUNNING | RESERVED | LLM Research | H100 SXM | 64 | 2/27/2026 | $2,340 | ||
| bge-embed-109 | RUNNING | ELASTIC | Embeddings | H100 SXM | 8 | 2/27/2026 | $412 | ||
| vit-pretrain-l16 | RUNNING | RESERVED | Vision | H100 SXM | 16 | 2/27/2026 | $890 | ||
| whisper-ft-v3 | RUNNING | ELASTIC | Speech | H100 SXM | 4 | 2/27/2026 | $156 | ||
| codegen-sft-13b | RUNNING | RESERVED | Code Gen | H100 SXM | 32 | 2/26/2026 | $4,120 | ||
| clip-align-xl | QUEUED | ELASTIC | Multimodal | H100 SXM | 32 | 2/27/2026 | — | ||
| reward-model-v4 | QUEUED | ELASTIC | RLHF | H100 SXM | 8 | 2/27/2026 | — | ||
| reward-train | FAILEDWhy? | ELASTIC | RLHF | H100 SXM | 8 | 2/26/2026 | $86 | ||
| dpo-align-7b | FAILED | RESERVED | Alignment | H100 SXM | 16 | 2/24/2026 | $1,240 | ||
| gpt-neo-eval | COMPLETED | ELASTIC | Evaluation | H100 SXM | 4 | 2/26/2026 | $58 | ||
| t5-summary-v2 | COMPLETED | ELASTIC | Summarization | H100 SXM | 8 | 2/26/2026 | $445 | ||
| bert-cls-ft | COMPLETED | RESERVED | NLP Prod | H100 SXM | 8 | 2/25/2026 | $310 | ||
| mistral-merge | COMPLETED | RESERVED | LLM Research | H100 SXM | 4 | 2/24/2026 | $124 |
Your team is spending too much time babysitting infra.
Workloads fail silently. Root-causing means digging through logs, metrics, and scheduler events across tools.
GPUs sit idle in one cluster while jobs queue in another. No way to balance capacity across clouds.
Getting the right outcomes for training jobs means correlating model experiment metrics with infrastructure metrics, and running many manual iterations to get there.
Give your ML team hours back every week.
While running more on existing GPUs.
Observe & Debug
Full GPU workload observability with automatic performance insights and root cause analysis. Find the issue in seconds, not hours.
Schedule & Optimize
Advanced cross-cloud scheduling maximizes GPU availability and utilization. Run more on the infrastructure you already have.
Iterate & Ship Faster
Chamber connects experiment metrics to infrastructure data and uses agents to help you iterate faster. Analyze runs, tune resources, and resubmit jobs automatically using our CLI, SDKs, or even in Slack. We work where you work.
Frequently Asked Questions
How long does it take to set up Chamber?
We handle deployment for you. Our team gets Chamber running in your environment, whether that's Kubernetes, Slurm, or a hybrid setup, with zero disruption to existing workflows.
Is my data secure?
Yes. Chamber is SOC 2 Type I certified. It runs within your infrastructure. Your models, datasets, and code never leave your environment.
What infrastructure do you support?
Multi-cloud and on-prem. Chamber works with AWS, GCP, Azure, on-prem clusters, Slurm, and Kubernetes, including hybrid setups across all of them.