LLM Inference Frameworks Leave Most GPU Bandwidth Untapped
Conventional LLM inference stacks dispatch one kernel per operation, resulting in hundreds of kernel launches per token, repeated CPU round-trips, and significant memory re-fetching — leaving the majority of available GPU compute and bandwidth unused. This affects developers and researchers running local or self-hosted inference on consumer and prosumer NVIDIA hardware. The gap between theoretical hardware capability and realized throughput is large, but this post is primarily a project announcement rather than a problem statement from users experiencing pain.
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Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.