Multi-GPU Inference Orchestration
Deep learning inference requires orchestrating Tensor Parallelism (TP), Pipeline Parallelism (PP), and Data Parallelism (DP) to fit massive models.
Source: mortalapps.com- Deep learning inference requires orchestrating Tensor Parallelism (TP), Pipeline Parallelism (PP), and Data Parallelism (DP) to fit massive models.
- TP slices layers and requires sub-millisecond NVLink; PP slices the network sequentially and tolerates slower interconnects.
- Advanced orchestrators route network topologies and manage global state dynamically to prevent bottlenecks.
Why This Matters
A single NVIDIA H100 possesses 80GB of VRAM. A LLaMA-3 70B model operating in FP16 demands roughly 140GB just for weights, completely ignoring the KV cache. Single-GPU inference is physically impossible at the frontier. Orchestrating across, 64, or 1024 GPUs requires mapping mathematical operations strictly to the underlying hardware physics.
Core Intuition
Imagine building a skyscraper. TP is 8 workers lifting a single massive steel beam simultaneously—they must be highly synchronized (NVLink). PP is an assembly line where Worker A finishes the 1st floor and hands the schematics up to Worker B for the 2nd floor—they only communicate at the handoff point, requiring less synchronization but careful timing (InfiniBand/PCIe).
Technical Deep Dive
Tensor Parallelism (TP): Slices matrices linearly. During the forward pass, each GPU computes a fraction of the matrix. At the end of every attention layer, an AllReduce operation triggers over the network to aggregate the tensors. Because this happens 80+ times per pass, TP is hyper-sensitive to latency and is strictly limited to intra-node NVLink environments (600 GB/s). Pipeline Parallelism (PP): Places groups of layers across different nodes. Operates effectively over InfiniBand or standard networking, but introduces pipeline bubbles that must be combatted with interleaving or temporal disaggregation. Disaggregated Orchestration: Central routers must manage peer-to-peer RDMA transfers of KV caches across physical boundaries while load-balancing incoming API queries.