The isolation hurdle in multi-tenant LLM inference is driving cloud-scale debates. The hot take: hardware partitioning (MIG) on NVIDIA GPUs vs runtime isolation stacks like vLLM and Punica [1][2].
The Core Debate How do you run multiple models for different customers on a single GPU without noisy neighbors, wasted headroom, or costly cold starts? Hardware isolation helps, but it’s coarse and inflexible; runtime scheduling can boost utilization yet may hurt latency and context consistency [1][2].
Paths in Play - MIG - hardware isolation, but often too coarse for real-time multi-tenant loads [2]. - vLLM - dynamic scheduling to improve utilization [2]. - Punica - runtime scheduling to shift load more smoothly [2]. - LoRA multiplexing - a related approach, with tradeoffs in latency and state management [2]. - Hybrid runtimes that dynamically allocate GPU slices per request type are a popular idea pending maturity [2]. - The dream: an AWS-like inference layer where the runtime heals latency and preserves isolation [2].
Multi-GPU Layout Realities One study line notes multi-GPU setups with limited PCIe bandwidth can bottleneck model loading, and sharing memory across GPUs gets messy fast [3]. Tensor parallelism across GPUs is sub-optimal due to constant cross-GPU talk, so some folks imagine divorcing tasks per GPU and recombining later—but that’s non-trivial [3].
Closing takeaway The prize goes to a runtime-first approach that delivers near-dedicated latency without static partitions.
References
The multi-tenant inference cloud is coming. Who's actually solving GPU isolation?
Discusses GPU isolation for multi-tenant LLM inference; argues runtime-level isolation beats hardware multiplexing; mentions ComfyUI and latency concerns, throughput challenges.
View source[D] The "Multi-Tenant Inference Cloud" is the next AI infrastructure battle. Is anyone actually solving the isolation problem?
Examines multi-tenant LLM inference isolation, compares MIG hardware partitioning with dynamic runtimes (vLLM, Punica, LoRA) seeking scalable, low-latency, global solutions.
View sourceIndividual models (or data sets) for multi-GPU setups using nerfed PCI-E lane options?
Discussion of running LLMs across multi-GPU PCIe lanes, loading times, keeping models loaded, tensor parallelism, and task-division strategies for efficiency.
View source