Distributed ML Serving
Pure Edge Computation
Transformer KV Cache VRAM Allocation Modeler
Models High-Bandwidth Memory (HBM) consumption for transformer attention layers during long-context serving.
Formula:
VRAM_token = 2 * Layers * Heads * HeadDim * Bytes_precision
Model Parameters
Outputs & Underwriting Summary
Calculation Status
MEMORY FOOTPRINT CONSTRAINED
Verified Model
Total KV Cache VRAM
312.50 GB
Per Stream: 19.53 GB
8x H100 Cluster Max Concurrency
25 streams
Bytes per Token: 163840 bytes
Each active stream requires 19.53 GB VRAM for KV cache. An 8x H100 cluster accommodates up to 25 concurrent streams at 128,000 context.
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