What challenges did Outpost VFX face before AI implementation?

Single-GPU limits created week-long training cycles and bottlenecks in face replacement model iteration. Slow training delayed director approval and could increase costs in VFX production schedules. The solution had to handle highly sensitive production data within a secure environment.

What was the impact of the AI solution?

Achieved up to 8x faster face replacement model learning speeds. Reduced initial v001 client review delivery to 2 days from the previous 1-2 weeks. Enabled use of higher-resolution images and larger datasets, improving output quality.

Source

Evidence: Low35/100

Outpost VFX accelerates face replacement model training using AWS EC2 multi-GPU on SageMaker AI

Use case typeAI model trainingUpdated Jun 30, 2026

Outpost VFX is a media and entertainment company delivering high-end film and episodic content across studios in the UK, Canada, and India. The team adapted its face swap model codebase to support distributed GPU training across multiple GPUs on AWS EC2 P5 instances, using AWS SageMaker AI context and help from the AWS Generative AI Innovation Center. The architecture ran in a segregated secure AWS environment and enabled faster iteration, higher-resolution images, and larger datasets for the face replacement workflow.

Organization: Outpost VFX
Industry: Tech & Comms
Location: United Kingdom
Published: June 2026

Reported outcomes

Face replacement model learning speed: Up to 8×

Time & speed

V001 client review delivery time: 1–2%

Strategic outcomes

Speed & agilityShortened iteration cycles for face replacement workflowScale & capacityEnabled larger datasets and higher-resolution imagesOther strategic outcomeKept sensitive production data in a segregated secure AWS environment

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Face replacement model learning speed: 8 x increase

AWS Machine Learning BlogJun 30, 2026Blog postExplicit claimLow evidence strength

achieved up to 8x improvement in face replacement model learning speeds

Normalized claim

V001 client review delivery time: 1-2%

AWS Machine Learning BlogJun 30, 2026Blog postInferred claimLow evidence strength

v001 delivery to clients for initial review now takes 2 days, compared to the previous 1–2 week timeline

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Outpost VFX
Provider: AWS
Maturity: Unknown
Linked source: AWS Machine Learning Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 2 of 2

1AI model training
2Training infrastructure modernization

Single-GPU limits created week-long training cycles and bottlenecks in face replacement model iteration.
Slow training delayed director approval and could increase costs in VFX production schedules.
The solution had to handle highly sensitive production data within a secure environment.

Outpost VFX migrated from local workstation-based training to AWS multi-GPU Amazon EC2 P5 instances with NVIDIA H100 GPUs.
AWS scientists and engineers helped convert the model code to PyTorch Distributed Data Parallel so training could run across multiple GPUs.
The team used a segregated secure AWS environment aligned to Outpost VFX's infrastructure requirements.

Technologies

Amazon SageMaker AI Amazon EC2 G5 Instances PyTorch Distributed Data Parallel NVIDIA H100 GPUs

Achieved up to 8x faster face replacement model learning speeds.
Reduced initial v001 client review delivery to 2 days from the previous 1-2 weeks.
Enabled use of higher-resolution images and larger datasets, improving output quality.

Architecture

The solution adapted the existing face swap model codebase for distributed training with PyTorch Distributed Data Parallel on Amazon EC2 P5 instances (NVIDIA H100 GPUs), running in a segregated secure AWS environment and supported by AWS Generative AI Innovation Center advisors.

Sources & evidence1

Evidence: Low35/100Evidence strength