AI Model Registry and Versioning: Lifecycle Management for GPU Deployments

A model registry is the single source of truth for every model version, its training provenance, its performance characteristics, and its deployment status. For GPU infrastructure, the registry is particularly important because each model version has specific GPU requirements (memory footprint, preferred generation, tensor parallelism configuration) that determine which hardware it can run on.

Without a registry, teams lose track of which model version is deployed where, what data it was trained on, and whether a newer version should replace it. In a multi-team organisation with 20+ models across development, staging, and production environments, the operational chaos of manual model tracking leads to deployment errors, compatibility issues, and extended incident resolution times.

This post covers model registry architecture, versioning strategies, lifecycle management, and integration with GPU infrastructure for production AI deployments at mid-2026.

A production model registry stores both model artefacts (weight files, tokenizer config, inference server configuration) and metadata (training run ID, dataset version, hyperparameters, evaluation metrics, GPU hardware requirements). The metadata is as important as the artefacts because it enables informed decisions about which model version to deploy and on which hardware.

The registry structure includes: model name (logical identifier for the model family), version (semantic versioning or timestamp-based), stage (development, staging, production, archived), artefact location (S3 or cloud storage path to model weights), GPU requirements (minimum GPU generation, memory footprint, supported precision formats), evaluation metrics (accuracy, latency P50/P95, throughput, memory usage), lineage (parent model or training run, dataset version, fine-tuning base), and deployment history (which environments and endpoints have run this version).

At mid-2026, MLflow remains the most widely deployed model registry (approximately 45% market share), followed by Hugging Face Hub for open-source models (30%), Weights & Biases (15%), and custom implementations (10%). The critical feature for GPU infrastructure is hardware-aware registration -- tagging each model version with its validated GPU requirements so deployment automation can select appropriate hardware.

Model versioning is more nuanced than software versioning because model changes are not always monotonic. A fine-tuned model may improve on one benchmark while regressing on another, making semantic versioning challenging. The recommended approach at mid-2026 is timestamp-based versioning combined with structured model naming that captures the architecture and training context.

The naming convention should include: model family (gpt, llama, qwen), parameter count (7B, 70B), training type (pretrain, sft, rlhf), and version timestamp. Example: `llama-3-70b-sft-20260615`. Within the registry, each named model has an ordered sequence of versions, and each version has immutable artefacts and metadata.

Immutability is critical. Once a model version is registered, its artefacts and metadata must not be modified. This ensures reproducibility: a deployment referencing model version `v3` always deploys the exact same weights. If a model is retrained with a different dataset or hyperparameters, it is registered as a new version. The registry supports model lineage tracking, showing the parent version and the transformation (fine-tuning, quantization, pruning) applied.

Model lifecycle in production typically follows four stages. Development: model is being iterated on, metrics are preliminary, no deployment commitment. Staging: model has passed candidate validation, deployed to staging environment for integration testing. Production: model is serving live traffic, covered by SLAs, monitored for drift. Archived: model is retired, artefacts preserved for reproducibility, no active deployment.

Promotion between stages should follow automated gates. Development to staging requires: accuracy within 1% of current production model on the evaluation set, GPU memory footprint validated, and inference latency profiled. Staging to production requires: staging deployment stable for 24+ hours, canary test passes in production environment (if applicable), and compliance review completed (for regulated industries).

The registry enforces these promotion rules through stage transition policies. For example, a model cannot be promoted to production without a registered staging deployment and a passing canary evaluation. These policies are typically implemented as CI/CD pipeline gates that query the registry's stage API before allowing deployment.

Model artefacts for large models are substantial. A 70B-parameter model in FP16 is 140 GB. In FP8, 70 GB. An INT4 quantized version is 35 GB. A single model family may have 5-10 artefact variants (different precision formats, optimization configurations), consuming 200-700 GB of storage per model family. With 10-20 model families, artefact storage reaches 2-14 TB.

Artifact storage architecture uses cost-tiered storage: hot storage (S3 standard or equivalent) for actively deployed model versions, warm storage (S3 infrequent access) for staging and recently superseded versions, and cold storage (S3 Glacier or equivalent) for archived versions that must be preserved for compliance but are not actively deployed.

Model artefact transfer to GPU instances is a significant time factor in deployment automation. Transferring a 140 GB model from S3 to a GPU instance over 25 Gbps networking takes approximately 45-60 seconds. Over 10 Gbps (common for some data centre interconnects), transfer takes 2-3 minutes. The CI/CD pipeline must account for this transfer time in the deployment timeout configuration.

The model registry connects to GPU orchestrators (Kubernetes, Slurm, Ray) through a deployment controller that reads model metadata from the registry and provisions the appropriate GPU infrastructure. The controller uses the registry to determine: which GPU generation is required, how many GPUs are needed based on tensor parallelism configuration, what container image and runtime configuration to use, and what validation checks to run before routing traffic.

A production deployment workflow: Data scientist registers model `llama-3-70b-sft-v3` with metadata specifying FP8 precision, validated on H100, minimum 80 GB memory. The CI/CD pipeline reads this metadata, selects an H100 GPU instance from the pool, deploys the model container, runs validation, and registers the deployment in the registry as stage=staging. After canary validation, the registry promotes the model to production and the load balancer routes traffic to the new deployment.

This integration ensures that deployment automation is self-service for data scientists. They register the model with the appropriate metadata, and the infrastructure automation handles the rest. The bottleneck shifts from infrastructure engineering cycles to model quality validation, enabling faster iteration.

For regulated industries, the model registry serves as the audit trail for every model in production. The registry records who created each model version, what data was used for training, what validation tests were passed, and which environments the model was deployed to and for how long. This provenance is essential for SOC2, HIPAA, and emerging AI regulations.

The 2026 EU AI Act compliance requirements for high-risk AI systems include: model version control (demonstrating which model version is operating at any point in time), training data provenance (showing the dataset version and preprocessing applied), performance monitoring (tracking model accuracy over time to detect drift), and human oversight (recording when human review was triggered and the outcome).

All of these requirements map directly to model registry functionality. Organisations that have invested in a robust registry with immutable versioning and comprehensive metadata collection are well-positioned for the evolving regulatory landscape. Those relying on ad-hoc model tracking face costly retrofitting projects to achieve compliance.