HAMi review
A focused Kubernetes layer for sharing, isolating, and scheduling heterogeneous accelerators.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
HAMi is an open-source Kubernetes platform for teams managing shared AI infrastructure. It allocates portions of GPUs and other accelerators across containers, applies per-workload memory and compute isolation, and enforces quotas and resource limits. Support covers NVIDIA GPUs, NPUs, HCUs, MLUs, and other heterogeneous accelerator types, making it relevant to platform teams running training, inference, notebooks, and private-cloud workloads. The deployment model is self-hosted, with installation through Helm in user-managed Kubernetes clusters.
Its strongest technical distinction is fractional accelerator management combined with device-aware scheduling. Teams can allocate resources by memory, compute core, or device count, while scheduling policies include binpack, spread, and topology-aware options. Dynamic NVIDIA MIG allocation adds another layer of partitioning for supported environments. These controls suit multi-tenant clusters where workloads need defined accelerator boundaries rather than whole-device allocation. Integrations with kube-scheduler, Volcano, and Kueue provide fit with established Kubernetes scheduling approaches, while NVIDIA GPU Operator support connects HAMi to a GPU-focused operating model.
Observability is another practical strength. HAMi exposes Prometheus-compatible utilization and allocation metrics, supports Grafana, and includes HAMi-WebUI for cluster and device visibility. Its ecosystem also includes vLLM, which is relevant to inference workloads. The trade-off is scope: HAMi specializes in accelerator virtualization, scheduling, quotas, and metrics instead of presenting a broader cluster-management platform. Teams that already operate Kubernetes and need fine-grained sharing across heterogeneous devices should consider it; organizations seeking a fully managed service or a wider infrastructure platform should look elsewhere.
HAMi pros and cons
- Where it wins
- Allocates accelerator memory, compute cores, or device counts fractionally
- Schedules across heterogeneous devices with topology-aware policies
- Connects metrics and observability through Prometheus, Grafana, and HAMi-WebUI
- Where it doesn't
- Requires a user-managed, self-hosted Kubernetes environment
- Focuses on accelerator management rather than a full cluster platform
- Operational scope depends on Kubernetes scheduling components and integrations
HAMi fact sheet, pricing and score →
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