What Are the MIG Profiles Available for A100 GPUs?

Introduction: Why GPU Sharing Has Become a Big Deal in the Cloud Era

Over the last few years, cloud infrastructure has changed dramatically. According to industry reports, more than 65% of AI and machine learning workloads today run on shared cloud environments, not on dedicated, single-tenant servers. As organizations deploy more AI-powered applications—chatbots, recommendation engines, fraud detection systems, and analytics pipelines—the pressure on GPU resources has increased sharply.

This is where efficient GPU utilization becomes critical. Owning or renting powerful GPUs like NVIDIA A100 is expensive, and leaving capacity unused is no longer an option. To solve this exact problem, NVIDIA introduced Multi-Instance GPU (MIG) technology with A100 GPUs. MIG allows a single A100 GPU to be securely divided into multiple smaller GPU instances, each behaving like an independent GPU.

But here’s where many teams get stuck: What MIG profiles are actually available for A100 GPUs, and how do you choose the right one for your cloud or server workload? This blog answers that question in detail, breaking down MIG profiles in a way that’s practical, conversational, and easy to apply to real-world cloud hosting and server environments.

Understanding MIG on A100 GPUs: A Quick Foundation

Before diving into specific MIG profiles, it’s important to understand what MIG really does.

MIG enables a single A100 GPU to be partitioned into multiple isolated GPU instances, each with:

- Dedicated compute cores

- Dedicated memory

- Dedicated cache

- Predictable performance

From a cloud hosting perspective, this is a game-changer. It allows providers to offer GPU-backed servers to multiple users without performance interference, while enterprises can run multiple AI workloads on the same physical server with confidence.

MIG is supported on A100 GPUs with sufficient memory capacity and is widely used in:

- Public cloud platforms

- Private cloud environments

- AI inference servers

- Multi-tenant enterprise infrastructure

How MIG Profiles Are Structured on A100 GPUs

MIG profiles are defined using two main parameters:

1. GPU slices (compute instances)

2. Memory slices

Each MIG profile is represented in a format like Xg.Ygb, where:

- Xg indicates the number of GPU slices

- Ygb indicates the amount of GPU memory allocated

These profiles allow precise control over how much compute and memory each workload receives, making them ideal for modern cloud and server deployments.

Full List of MIG Profiles Available for A100 GPUs

The A100 GPU supports several MIG profiles designed to suit different workloads, from lightweight inference to more demanding AI services. Below is a detailed look at the commonly available profiles.

1g.5gb MIG Profile: Lightweight Inference and Microservices

The 1g.5gb profile is the smallest MIG configuration available on A100 GPUs.

Key Characteristics

- 1 GPU slice

- 5 GB of GPU memory

- Ideal for low-intensity workloads

Best Use Cases

This profile works well for:

- Small AI inference tasks

- Lightweight ML models

- API-based AI services

- Development and testing environments

In cloud hosting setups, this profile is often used to serve multiple small clients on a single server, maximizing GPU utilization without overprovisioning resources.

2g.10gb MIG Profile: Balanced Performance for Moderate Workloads

The 2g.10gb profile offers a step up in both compute and memory, making it suitable for more demanding inference workloads.

Key Characteristics

- 2 GPU slices

- 10 GB of GPU memory

- Balanced compute-to-memory ratio

Best Use Cases

This profile is commonly used for:

- Medium-sized AI inference pipelines

- Computer vision applications

- NLP models with moderate memory requirements

- Cloud-hosted AI services with consistent traffic

For cloud providers, this profile strikes a sweet spot between performance and density on a single server.

3g.20gb MIG Profile: High-Throughput Inference and Analytics

The 3g.20gb MIG profile is designed for workloads that require higher throughput and more memory headroom.

Key Characteristics

- 3 GPU slices

- 20 GB of GPU memory

- Strong performance consistency

Best Use Cases

This profile is ideal for:

- High-volume inference workloads

- Data analytics pipelines

- Recommendation systems

- AI workloads running continuously on cloud servers

Enterprises often choose this profile when they want predictable performance without dedicating a full A100 GPU to a single workload.

4g.20gb MIG Profile: Compute-Heavy, Memory-Efficient Tasks

The 4g.20gb profile focuses more on compute density rather than memory expansion.

Key Characteristics

- 4 GPU slices

- 20 GB of GPU memory

- Higher compute allocation per instance

Best Use Cases

This profile works well for:

- Compute-intensive inference tasks

- Parallel processing workloads

- Real-time AI services with strict latency requirements

In cloud hosting environments, this profile is useful when memory requirements are stable but compute demand fluctuates.

7g.40gb MIG Profile: Near Full-GPU Performance

The 7g.40gb profile is the largest MIG configuration available on A100 GPUs.

Key Characteristics

- 7 GPU slices

- 40 GB of GPU memory

- Performance close to a full A100 GPU

Best Use Cases

This profile is typically used for:

- Large AI inference workloads

- Multi-model serving

- Advanced analytics

- High-performance enterprise applications

Cloud providers often reserve this profile for premium GPU-backed server offerings where customers need near-dedicated GPU performance without full isolation.

How MIG Profiles Improve Cloud Hosting Efficiency

From a cloud infrastructure perspective, MIG profiles fundamentally change how GPUs are consumed.

Instead of allocating one full GPU per workload, cloud hosting platforms can:

- Offer granular GPU instances

- Improve server density

- Reduce idle GPU capacity

- Lower overall infrastructure costs

For enterprises, this means better ROI on GPU investments and the ability to scale AI workloads more flexibly across servers.

Choosing the Right MIG Profile for Your Workload

Selecting the right MIG profile depends on several factors:

Workload Type

- Inference-heavy workloads benefit from smaller, multiple MIG instances

- Analytics and streaming workloads may require mid-sized profiles

- High-demand applications may need larger profiles

Memory Requirements

Models with large parameter sizes require profiles with higher memory allocation, while smaller models can run efficiently on lower-memory profiles.

Cloud and Server Strategy

In multi-tenant cloud environments, smaller MIG profiles allow better resource sharing. In private cloud or enterprise server setups, larger profiles may provide better performance isolation.

MIG Profiles and Server-Level Resource Planning

MIG profiles also simplify server planning. Infrastructure teams can:

- Predict resource usage more accurately

- Assign fixed GPU resources per application

- Avoid noisy-neighbor problems in shared environments

This predictability is one of the biggest reasons MIG adoption has accelerated across cloud hosting platforms.

Security and Isolation Benefits of MIG

One often overlooked advantage of MIG is hardware-level isolation. Each MIG instance is isolated at the GPU level, which is especially important in:

- Multi-tenant cloud hosting

- Regulated industries

- Enterprise AI platforms handling sensitive data

This makes MIG-backed A100 servers suitable for industries like finance, healthcare, and SaaS platforms.

Conclusion: MIG Profiles Make A100 GPUs Cloud-Ready

MIG profiles are not just a technical feature—they are a strategic advantage for modern cloud and server infrastructure. By allowing a single A100 GPU to be divided into multiple isolated instances, MIG enables better utilization, predictable performance, and cost-efficient scaling.

Whether you’re running lightweight inference workloads, high-throughput analytics, or enterprise-grade AI as a services, A100 MIG profiles provide the flexibility to match GPU resources precisely to workload demands. For cloud hosting providers and enterprises alike, understanding and leveraging these MIG profiles is key to building scalable, efficient, and future-ready AI infrastructure.

In an era where every GPU cycle matters, MIG transforms A100 GPUs from powerful hardware into truly adaptable cloud-ready assets.