VQV Signal
LLMVisor is a latency attribution model designed for multi-tenant GPU clusters running large language model inference. It provides accurate, lightweight per-request latency attribution suitable for real-time scheduling and fractional resource sharing.
LLMVisor is a latency attribution model designed for multi-tenant GPU clusters running large language model inference. It provides accurate, lightweight per-request latency attribution suitable for real-time scheduling and fractional resource sharing.
AI-assisted summary based on the listed source.
As LLM inference shifts to multi-tenant GPU clusters, co-batching improves throughput but obscures per-tenant usage and limits control. Enabling fractional sharing of the inference engine requires a real-time, per-request attribution primitive that is accurate and light enough to run inside the scheduling loop. We...
As multi-tenant LLM inference grows, understanding per-tenant latency is critical for efficient resource allocation and control. LLMVisor addresses this by enabling precise latency tracking within the scheduling loop, improving throughput management.
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