trtllm-dp-rank-routing.md

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DP Rank Routing (Attention Data Parallelism)

For general TensorRT-LLM features and configuration, see the Reference Guide.

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TensorRT-LLM supports attention data parallelism (attention DP) for models like DeepSeek. When enabled, multiple attention DP ranks run within a single worker, each with its own KV cache. Dynamo can route requests to specific DP ranks based on KV cache state.

Dynamo vs TRT-LLM Internal Routing

• Dynamo DP Rank Routing: The router selects the optimal DP rank based on KV cache overlap and instructs TRT-LLM to use that rank with strict routing (attention_dp_relax=False). Use this with --router-mode kv for cache-aware routing.
• TRT-LLM Internal Routing: TRT-LLM's scheduler assigns DP ranks internally. Use this with --router-mode round-robin or random when KV-aware routing isn't needed.

Enabling DP Rank Routing

# Worker with attention DP
# (TP=2 acts as the "world size", in effect creating 2 attention DP ranks)
CUDA_VISIBLE_DEVICES=0,1 python3 -m dynamo.trtllm \
  --model-path <MODEL_PATH> \
  --tensor-parallel-size 2 \
  --enable-attention-dp \
  --publish-events-and-metrics

# Frontend with KV routing
python3 -m dynamo.frontend --router-mode kv

The --enable-attention-dp flag sets attention_dp_size = tensor_parallel_size and configures Dynamo to publish KV events per DP rank. The router automatically creates routing targets for each (worker_id, dp_rank) combination.

Attention DP requires TRT-LLM's PyTorch backend. AutoDeploy does not support attention DP.