Deployment Tiers
On-Demand
Flexible, pay-as-you-go GPU resources for dynamic workloads.
Reserved
Dedicated GPU clusters for consistent, high-volume workloads.
Serverless
Auto-scaling deployments with no infrastructure management (Coming Soon).
Deploying On-Demand
Navigate to Deployments → On-Demand from the sidebar to begin.
Cloud Configuration
Configure your hardware environment to match your model’s requirements.Cloud Provider — Select your preferred provider
Region — Choose a geographic location for your deployment
GPU Type — Select the specific GPU model
GPUs per Replica — Specify the number of GPUs assigned to each instance
Number of Replicas — Set how many parallel instances to run to handle traffic volume
Deployment Name — Provide a custom name or leave empty for an auto-generated ID (optional)
Region — Choose a geographic location for your deployment
GPU Type — Select the specific GPU model
GPUs per Replica — Specify the number of GPUs assigned to each instance
Number of Replicas — Set how many parallel instances to run to handle traffic volume
Deployment Name — Provide a custom name or leave empty for an auto-generated ID (optional)
Model Source
Tensormesh supports multiple sources for your models.Model Library — Choose from a curated, pre-optimized model collection
HuggingFace — Deploy directly from the HuggingFace Hub (provide repository ID and token for private models)
S3 Cloud Storage — Deploy custom or fine-tuned models from your private storage (Coming Soon)
HuggingFace — Deploy directly from the HuggingFace Hub (provide repository ID and token for private models)
S3 Cloud Storage — Deploy custom or fine-tuned models from your private storage (Coming Soon)
Advanced Configuration
Optimize your inference engine with specialized caching and offloading settings.
CPU Offloading
Default: EnabledOffloads the KV cache to CPU memory to manage larger contexts.
Storage Offloading
(Coming Soon)Will allow offloading the KV cache to external storage.
Review System Specifications
Review the hardware capabilities of your selection in the side panel.Architecture — The underlying GPU architecture
Memory — Total HBM available
Bandwidth — Data transfer rate
Interconnect — GPU-to-GPU communication technology
Memory — Total HBM available
Bandwidth — Data transfer rate
Interconnect — GPU-to-GPU communication technology
Cost Summary & Launch
The Cost Summary panel provides real-time estimates based on your configuration.Hourly Cost — The real-time rate per hour
Monthly Estimate — Projected cost for a full month of continuous operationOnce all required configuration steps are complete, click Deploy Model to initiate provisioning.
Monthly Estimate — Projected cost for a full month of continuous operationOnce all required configuration steps are complete, click Deploy Model to initiate provisioning.
Reserved Deployments
For large-scale workloads and enterprise-grade performance, Reserved Deployments provide dedicated GPU clusters tailored to your specific infrastructure needs. Navigate to Deployments → Reserved to request a tailored capacity plan.
Requesting a Cluster
To initiate a reserved deployment, provide your cluster specifications through the request form. GPU Selection — Choose between high-compute optionsCluster Size — Define the total number of GPUs required for your workload
Timeline — Specify your deployment window
Use Case — Define the intention for the cluster
Use the “Additional Requirements” field to specify custom networking needs, storage bandwidth targets, or specific compliance and SLA requirements.