How to Partition AMD GPUs for Shared AI Compute
A five-step how-to for deciding when an AMD Instinct GPU should stay dedicated and when SR-IOV virtual functions, VMs, or sidecar isolation can safely share it.
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A five-step how-to for deciding when an AMD Instinct GPU should stay dedicated and when SR-IOV virtual functions, VMs, or sidecar isolation can safely share it.
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Modal introduces Sidecars, isolated containers that run alongside a main Sandbox on the same host, establishing a trust boundary between trusted and untrusted code. Sidecars enable communication across trust boundaries at least 3x faster than using separate Sandboxes, reducing latency for operation-heavy agent workloads. The feature addresses the security risk of running an agent’s harness and tool calls together, which creates the “lethal trifecta” of private data, untrusted content, and external communication. Existing solutions either move the harness to a remote service (adding network latency) or rely on egress controls that lack flexibility. Sidecars allow trusted services such as credential injection, proxy traffic, and harness logic to run locally alongside the Sandbox, with independent network policies and dynamic creation via the Modal SDK.
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