GRID: Building a Generalized Runtime for Inference & Deployment at Scale

This third installment in the MasterControl product team's artificial intelligence (AI) blog series takes a deep dive into GRID, MasterControl's proprietary internal large language model (LLM) management platform. For further insight into MasterControl's AI innovations, you're invited to read the first and second blog posts in the series.

Generalized Runtime for Inference & Deployment (GRID) is MasterControl's proprietary internal platform for managing the complexity of deploying LLMs across diverse infrastructure — from multiple clouds and graphic processing unit (GPU) profiles to various inference runtimes. GRID acts as a unifying abstraction layer that enables our teams to focus on delivering domain-specific intelligence, while also handling the orchestration beneath the surface.

Read more to understand GRID's architecture, why we built it, how it works today, and what lies ahead.

The Problem Space

As MasterControl's AI capabilities have expanded, so has the complexity of our infrastructure. We found ourselves supporting multiple models — each with its own tokenizer, memory footprint, inference backend, and logging needs. Some models were optimized for low-latency completions, others for large-context reasoning. Inference frameworks varied too: we deployed using Virtual Large Language Model (vLLM) for general-purpose text generation and lightweight backends like llama.cpp for quantized models on the edge.

Our compute landscape was equally fragmented. We were juggling single-GPU nodes (like A10s) and multi-GPU clusters (like multiple A10s), and would like to have the capability to deploy workloads across AWS, Azure, and GCP. Each combination of model, runtime, cloud, and compute profile required bespoke configurations: provisioning, observability, security, versioning, resource scaling, and runtime tuning. Much of this was manually wired and fragile.

We needed a single platform to abstract this complexity, enforce consistent security and monitoring practices, and enable rapid experimentation without compromising uptime.

GRID: The Philosophy

GRID was built around a few foundational principles:

Separation of Concerns:

Model developers shouldn't be concerned with Kubernetes manifests, GPU sizing, or container networking. Likewise, Machine Learning Ops (MLOps) shouldn't need to know the quirks of specific tokenizers or generation parameters.

Runtime-Agnostic Deployment:

A model should be able to run on vLLM, Llama, or TGI based on the environment and use case — without changing its definition.

Cloud-Neutral Execution:

GRID runs on AWS now, but the same deployment spec can be instantiated on any cloud, depending on latency, cost, or regulatory needs.

Observability-First:

Every request is traced from input prompt to GPU utilization. Logs, traces, and metrics need to be captured. We can answer questions like: How long did this prompt process? How many tokens were batched? Which runtime processed it?

Scalability by Design:

GRID is built for flexible, scalable deployments. While automated scaling based on token queues and runtime-level metrics is on our roadmap, current scaling is intentionally manual. Teams adjust replicate counts and instance profiles based on usage patterns to maintain reliability and control costs.

GRID: A Layered Overview

At its core, GRID is organized into a series of purpose-built layers — each managing a different dimension of LLM deployment — topped by a Core Layer that delivers shared services like authentication, observability, and release automation. Together, they enable reliable, cloud-agnostic model execution at scale.

1. Model Abstraction Layer

All GRID deployments begin with a declarative model spec. Models are defined via a YAML descriptor that captures all deployment-critical metadata: model name, tokenizer path, inference backend, generation parameters, and runtime preferences etc. A sample configuration might look like this:

default_configuration:

model_config:
dtype: "float16"
hf_model_id: "llm_from_hugging_face"
model_inference_library: "vllm"
s3_model_store_path_nf: "mc-ml-us-west-2-dev"
hf_task: "text-generation"
generate_parameters:
max_tokens: 1000
temperature: 0.8
top_p: 0.9
model_kwargs:
tensor_parallel_size: 4
max_model_len: 20000
gpu_memory_utilization: 0.94

endpoint_config

..

.

All models are versioned and stored in our internal registry alongside tokenizers and other artifacts. This abstraction ensures that models are portable: a model defined once can be deployed across clouds, runtimes, or regions without major environment-specific customization — and always with full observability.

2. Inference Runtime Layer

This layer determines how the model is executed at runtime.

GRID supports a set of interchangeable inference engines, each optimized for different serving patterns:

• vLLM provides high-throughput, batched inference with streaming support and is our default engine for production endpoints.
• TGI (Text Generation Inference) from HuggingFace offers fine-grained control over generation settings, making it ideal for research and tokenizer-sensitive deployments.
• llama.cpp and GGUF are lightweight runtimes used for quantized models, especially in CPU-based or edge scenarios.

Every runtime is containerized with built-in telemetry and standardized health checks. GRID's infrastructure makes it easy to benchmark latency, hot-swap runtimes across environments (e.g., vLLM in prod, llama.cpp in staging).

3. Compute Resource Layer

This layer governs how we assign hardware to model workloads. GRID runs exclusively on GPU-backed EC2 instances and supports a range of configurations:

• g5.xlarge (A10 GPU) for lightweight endpoints.
• Or multiple smaller machines to create a cluster to run distributed inference.
• Or bigger machine like p5.24xlarge (multi-A100) for heavy batch or large-context models.

For models that require distributed execution, GRID supports multi-node deployment using LWS – Leader Worker Set, enabling horizontal scaling with tensor parallelism and pipeline parallelism.

Upcoming enhancements include:

• A vLLM-aware auto-scaler that responds to token queues and backlog.
• Karpenter-based GPU autoscaling for just-in-time provisioning of ephemeral nodes.

4. Cloud Execution Layer

Although GRID runs primarily on AWS today, it's architected for multi-cloud deployment.

Each supported cloud environment includes a Kubernetes cluster, GPU node pools, model registry, and observability stack. GRID abstracts cloud-specific nuances through:

• Helm + Terraform blueprints for repeatable provisioning.
• Consistent container naming conventions and deployment logic.

Deployments can be pinned to specific clouds or regions — for example, to meet data residency requirements — or dynamically burst to more cost-effective regions based on load.

Core Layer: The Platform Backbone

The Core Layer delivers the infrastructure and controls that make GRID production-ready.

All models are exposed via a unified API gateway. Each request is traced using OpenTelemetry, logged to Prometheus, and visualized in Grafana. We track end-to-end performance and correlate these metrics across services for debugging and optimization.

Deployment automation is powered by GitOps, using ArgoCD and Helm. Teams can promote, roll back, or preview model versions directly through pull requests, keeping deployment friction low and auditability high.

We're also building advanced capabilities in this layer, including token-based autoscaling, where decisions are driven by tokens-per-second or any custom metrics rather than request count — a more accurate signal for LLM load.

GRID in Action

GRID powers several of our most sophisticated use cases, including our Regulatory Chat feature, Production Record Master Template Generation, and other applications that we have developed. These applications demand strict control over behavior, performance, and scaling — all of which GRID handles seamlessly under the hood.

Looking Ahead

GRID is evolving into a general-purpose inference platform that supports both internal innovation and future external-facing applications.

Upcoming:

• Autoscaling driven by vLLM's token queue telemetry.
• A developer playground with real-time gRPC (Google's Remote Procedure Calls) streaming.
• Early support for serverless inference — enabling scale-to-zero endpoints for batch inference or experimentation.

Final Thoughts

GRID began as a pragmatic solution for managing LLM deployment complexity. Today, it's a foundational pillar of MasterControl's AI stack — empowering teams to iterate faster, deploy reliably, and scale intelligently.

By abstracting away runtime quirks, cloud variance, and infrastructure sprawl, GRID gives our engineers what they need most: a clear, reliable path from model to production.

Whether you're deploying one model or one hundred, on a single GPU or across three clouds, GRID is how we can make it work.