[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fqATYRrBiYIbwithN6NAqBdFVjwTyLFgbgTaU9jXgMCU":3},{"slug":4,"term":5,"shortDefinition":6,"seoTitle":7,"seoDescription":8,"explanation":9,"relatedTerms":10,"faq":20,"category":27},"multi-instance-gpu","Multi-Instance GPU","Multi-Instance GPU (MIG) is an NVIDIA technology that partitions a single GPU into multiple isolated instances, each with dedicated compute, memory, and cache resources.","Multi-Instance GPU in hardware - InsertChat","Learn what Multi-Instance GPU is, how it improves GPU utilization, and its benefits for AI inference serving. This hardware view keeps the explanation specific to the deployment context teams are actually comparing.","Multi-Instance GPU matters in hardware work because it changes how teams evaluate quality, risk, and operating discipline once an AI system leaves the whiteboard and starts handling real traffic. A strong page should therefore explain not only the definition, but also the workflow trade-offs, implementation choices, and practical signals that show whether Multi-Instance GPU is helping or creating new failure modes. Multi-Instance GPU (MIG) is an NVIDIA technology introduced with the A100 that allows a single physical GPU to be partitioned into up to seven independent GPU instances. Each instance has its own compute resources, memory, memory bandwidth, and cache, providing hardware-level isolation that guarantees quality of service for each workload.\n\nMIG addresses the common problem of GPU underutilization in inference scenarios. A single large language model inference request may only use a fraction of an A100 or H100's compute capacity. Without MIG, running multiple models on one GPU requires time-sharing with potential interference. MIG provides true hardware isolation, allowing different teams, models, or customers to share a single GPU without performance interference.\n\nMIG instances can be configured in various sizes: on an A100, options include 1g.5gb (1\u002F7th GPU), 2g.10gb (2\u002F7th), 3g.20gb (3\u002F7th), 4g.20gb (4\u002F7th), and 7g.40gb (full GPU). Each instance appears as a separate GPU to software, with its own device ID and compatibility with CUDA, Docker, and Kubernetes. MIG is particularly valuable in cloud and multi-tenant environments.\n\nMulti-Instance GPU is often easier to understand when you stop treating it as a dictionary entry and start looking at the operational question it answers. Teams normally encounter the term when they are deciding how to improve quality, lower risk, or make an AI workflow easier to manage after launch.\n\nThat is also why Multi-Instance GPU gets compared with A100, H100, and NVIDIA. The overlap can be real, but the practical difference usually sits in which part of the system changes once the concept is applied and which trade-off the team is willing to make.\n\nA useful explanation therefore needs to connect Multi-Instance GPU back to deployment choices. When the concept is framed in workflow terms, people can decide whether it belongs in their current system, whether it solves the right problem, and what it would change if they implemented it seriously.\n\nMulti-Instance GPU also tends to show up when teams are debugging disappointing outcomes in production. The concept gives them a way to explain why a system behaves the way it does, which options are still open, and where a smarter intervention would actually move the quality needle instead of creating more complexity.",[11,14,17],{"slug":12,"name":13},"gpu-virtualization","GPU Virtualization",{"slug":15,"name":16},"a100","A100",{"slug":18,"name":19},"h100","H100",[21,24],{"question":22,"answer":23},"Which NVIDIA GPUs support MIG?","MIG is supported on A100, A30, H100, H200, and Blackwell-generation data center GPUs. Consumer GPUs (RTX series) do not support MIG. The feature is designed for data center environments where GPU sharing and multi-tenancy are important for cost efficiency. Multi-Instance GPU becomes easier to evaluate when you look at the workflow around it rather than the label alone. In most teams, the concept matters because it changes answer quality, operator confidence, or the amount of cleanup that still lands on a human after the first automated response.",{"question":25,"answer":26},"Is MIG useful for AI training?","MIG is primarily designed for inference and multi-tenant sharing. For training, you generally want the full GPU resources. However, MIG can be useful for training multiple small experiments simultaneously or for development workflows where multiple users need GPU access on a shared server. That practical framing is why teams compare Multi-Instance GPU with A100, H100, and NVIDIA instead of memorizing definitions in isolation. The useful question is which trade-off the concept changes in production and how that trade-off shows up once the system is live.","hardware"]