back_office_ops · saas · workflow
LinkedIn reduces GPU memory usage by 60% for LLM training with Liger-Kernel
LinkedIn's LLM training at scale suffered from two performance bottlenecks: heavy GPU memory access from frequent data transfers between slow HBM and fast SRAM, and extra time and resources consumed per training operation.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Training bottleneck identified
LinkedIn experienced performance bottlenecks during LLM training, including heavy GPU memory access and extra time and resources used per-operation.
Tools used
Liger-KernelFlashAttentionPyTorchtorch.compileTritonTorch Distributed Elastic
Outcome
Liger-Kernel improved multi-GPU training throughput by 20%, reduced end-to-end training time by 3x, and reduced memory usage by 60%.
Results
Time saved3x reduction
Volume20%
Grounding & classification
Source type: technical build writeup
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failure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecycle time reductionthroughput increasetechnical build writeupback office ops