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POST
https://api.tensormesh.ai
/
v1
/
support
/
reserved-deployments
Submit Reserved Deployment
curl --request POST \
  --url https://api.tensormesh.ai/v1/support/reserved-deployments \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "fullName": "<string>",
  "workEmail": "<string>",
  "company": "<string>",
  "jobTitle": "<string>",
  "preferredGpu": "GPU_TYPE_UNSPECIFIED",
  "totalGpusNeeded": "<string>",
  "deploymentTimeline": "<string>",
  "workloadUseCase": "<string>",
  "networkingStorage": "<string>",
  "otherNotes": "<string>"
}
'
{
  "requestId": "<string>"
}

Documentation Index

Fetch the complete documentation index at: https://docs.tensormesh.ai/llms.txt

Use this file to discover all available pages before exploring further.

Authorizations

​
Authorization
string
header
required

Bearer authentication using an access token. Format: Bearer <access_token>

Body

application/json

SubmitReservedDeploymentRequest captures the reservation form fields.

​
fullName
string

Full name.

​
workEmail
string

Work email.

​
company
string

Company name.

​
jobTitle
string

Job title.

​
preferredGpu
enum<string>
default:GPU_TYPE_UNSPECIFIED

GPUType specifies the type of GPU to use for a model deployment.

This enum defines the supported GPU types for model deployments. It allows clients to specify the exact GPU hardware they need for their models.

enum definitions

Available options:
GPU_TYPE_UNSPECIFIED,
GPU_TYPE_A100,
GPU_TYPE_H100,
GPU_TYPE_H200,
GPU_TYPE_B200
​
totalGpusNeeded
string

Total GPUs needed (e.g. <8 GPUs).

​
deploymentTimeline
string

Deployment timeline (e.g. 1-3 months).

​
workloadUseCase
string

Workload/use case.

​
networkingStorage
string

Networking/storage requirements.

​
otherNotes
string

Additional notes.

Response

A successful response.

SubmitReservedDeploymentResponse is the response after submission.

​
requestId
string

Request tracking ID (optional).