---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 9ae9d5fc28cf73535220d601b4a437df6da64078147192f12697bdb84a4c7338
  pageDirectory: concepts
  sources:
    - provisioned-throughput-foundation-model-apis-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - gpu-capacity-limitations-in-model-deployment
    - GCLIMD
  citations:
    - file: provisioned-throughput-foundation-model-apis-databricks-on-aws.md
    - file: provenanced
title: GPU Capacity Limitations in Model Deployment
description: A known limitation where model deployment on Databricks may fail due to GPU capacity issues, causing timeout during endpoint creation or update.
tags:
  - databricks
  - deployment
  - limitation
timestamp: "2026-06-19T19:59:33.441Z"
---

# GPU Capacity Limitations in Model Deployment

**GPU Capacity Limitations in Model Deployment** refers to the constraint that occurs when deploying models to provisioned throughput serving endpoints on Databricks, where the requested inference capacity cannot be fulfilled due to insufficient GPU hardware resources in the available cluster or infrastructure. These limitations typically manifest as timeout errors during endpoint creation or update operations.

## Overview

When creating a provisioned throughput model serving endpoint, Databricks allocates dedicated inference capacity from available GPU resources to serve foundation models. The platform attempts to provision the requested number of [Model Units](/concepts/model-units.md) — each representing a specific throughput chunk — to meet the performance requirements of the production GenAI application. ^[provisioned-throughput-foundation-model-apis-databricks-on-aws.md]

However, model deployment may fail because the underlying GPU infrastructure cannot accommodate the requested allocation. This is most common when deploying large models that require significant memory or compute resources, or when multiple deployments compete for the same pool of GPUs. ^[provisioned-throughput-foundation-model-apis-databricks-on-aws.md]

## Symptoms of Capacity Limitations

When GPU capacity is insufficient, the deployment process does not proceed normally. Instead, users encounter:

- **Timeout during endpoint creation**: The API request to create the serving endpoint hangs and eventually fails with a timeout error, rather than returning a successful response.
- **Timeout during endpoint update**: Similarly, modifying an existing endpoint (e.g., scaling up provisioned throughput) may time out if the requested additional capacity is not available. ^[provisioned-throughput-foundation-model-apis-databricks-on-aws.md]

The limitation applies to both the UI-based endpoint creation and the REST API-based deployment workflows. In both cases, the platform cannot complete the resource allocation because the requested GPU capacity is not free at that moment. ^[provisioned-throughput-foundation-model-apis-databricks-on-aws.md]

## Root Cause

GPU capacity limitations arise because Databricks provisioned throughput endpoints operate on shared infrastructure where multiple tenants and workloads compete for the same hardware. The exact reasons include:

- **Resource contention**: Other high-priority workloads (training jobs, other serving endpoints) may consume the available GPU capacity, leaving insufficient resources for the new deployment.
- **Regional availability**: Certain GPU types (e.g., H100, A10) may have limited availability in specific cloud regions or during peak usage periods.
- **Model size**: Some foundation models require more GPU memory than is typical, making them harder to fit into the available capacity without specialized hardware configurations. ^[provenanced]

## Resolution

Because the platform cannot programmatically resolve GPU capacity shortages, the recommended action involves human intervention. Users should:

> **Reach out to your Databricks account team to help resolve.**

The Databricks account team can investigate the specific capacity situation, potentially:

- Prioritizing the deployment in the queue
- Coordinating with cloud providers to allocate additional GPU nodes
- Recommending alternative deployment strategies or scheduling during lower-demand periods ^[provisioned-throughput-foundation-model-apis-databricks-on-aws.md]

## Related Concepts

- [Provisioned Throughput](/concepts/provisioned-throughput.md) — The deployment mode that uses dedicated GPU capacity for consistent inference performance.
- [Model Units](/concepts/model-units.md) — The throughput increments in which provisioned capacity is allocated.
- [Foundation Model APIs](/concepts/foundation-model-apis.md) — The serving infrastructure for large language models on Databricks.
- GPU Capacity Planning — Strategies for managing GPU resource allocation across multiple workloads.
- [Serving Endpoint Timeout](/concepts/model-serving-endpoint-timeouts.md) — Specific timeout behavior during endpoint creation or modification.

## Sources

- provisioned-throughput-foundation-model-apis-databricks-on-aws.md

# Citations

1. [provisioned-throughput-foundation-model-apis-databricks-on-aws.md](/references/provisioned-throughput-foundation-model-apis-databricks-on-aws-0afb43fa.md)
2. provenanced
