Workflow · saas · workflow
Beyond accelerators: Lessons from building foundation models on AWS with Japan's GENIAC program
Allocating over 1,000 accelerators was merely the starting point—successful foundation model training at scale required far more than raw hardware, with the real challenges being reliable distributed systems architecture and cross-organizational coordination.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · GENIAC program launch
METI launched GENIAC as a national program to boost generative AI development and AWS was selected as the cloud provider for cycle 2.
Tools used
Amazon EC2 P5Amazon EC2 Trn1AWS ParallelClusterAmazon EKSAmazon S3Amazon FSx for LustreAmazon FSx for OpenZFSAWS CloudFormationAmazon Managed Service for PrometheusAmazon Managed GrafanaSlurmPyTorchNCCLDCGM ExporterEFA ExporterSlack
Outcome
Twelve customers deployed 127 EC2 P5 instances and 24 EC2 Trn1 instances in a single day, and over 6 months multiple models were trained successfully including a 32B multimodal model on Trainium and a 405B tourism-focused multilingual model.
Results
Time savedsingle day
Volume12
Grounding & classification
Source type: platform led case
37 fields verified against source quotes, 1 dropped as unverifiable.
failure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedgovernmentsoftwareemployee productivitythroughput increaseplatform led case