Deep-Dive: TensorRT-LLM Optimization & RoCE Networking in OCI Nemotron Clusters
Achieving **sub-20ms TTFT** in **NVIDIA Nemotron 3.5 Lightning** requires custom TensorRT-LLM execution graphs optimized for Hopper FP8 Tensor Cores. FlashAttention-3 kernels are fused directly with KV-cache quantization routines, eliminating memory bandwidth bottlenecks.
Key Takeaway & Industry Impact
A technical breakdown of TensorRT-LLM kernel fusion, FP8 quantization, and RoCE v2 networking behind OCI's Nemotron 3.5 deployment.
Over OCI's non-blocking RoCE v2 fabric, intra-node tensor parallelism executes with sub-microsecond latency. Distributed KV-caches are synchronized via RDMA offload engines, preventing network jitter during peak inference concurrency.
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