| title |
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Gemma3 Sliding Window |
For general TensorRT-LLM features and configuration, see the Reference Guide.
This guide demonstrates how to deploy google/gemma-3-1b-it with Variable Sliding Window Attention (VSWA) using Dynamo. Since google/gemma-3-1b-it is a small model, each aggregated, decode, or prefill worker only requires one H100 GPU or one GB200 GPU. VSWA is a mechanism in which a model’s layers alternate between multiple sliding window sizes. An example of this is Gemma 3, which incorporates both global attention layers and sliding window layers.
Note
- Ensure that required services such as
natsandetcdare running before starting. - Request access to
google/gemma-3-1b-iton Hugging Face and set yourHF_TOKENenvironment variable for authentication.
cd $DYNAMO_HOME/examples/backends/trtllm
export MODEL_PATH=google/gemma-3-1b-it
export SERVED_MODEL_NAME=$MODEL_PATH
export AGG_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_agg.yaml
./launch/agg.shcd $DYNAMO_HOME/examples/backends/trtllm
export MODEL_PATH=google/gemma-3-1b-it
export SERVED_MODEL_NAME=$MODEL_PATH
export AGG_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_agg.yaml
./launch/agg_router.shcd $DYNAMO_HOME/examples/backends/trtllm
export MODEL_PATH=google/gemma-3-1b-it
export SERVED_MODEL_NAME=$MODEL_PATH
export PREFILL_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_prefill.yaml
export DECODE_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_decode.yaml
./launch/disagg.shcd $DYNAMO_HOME/examples/backends/trtllm
export MODEL_PATH=google/gemma-3-1b-it
export SERVED_MODEL_NAME=$MODEL_PATH
export PREFILL_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_prefill.yaml
export DECODE_ENGINE_ARGS=$DYNAMO_HOME/examples/backends/trtllm/engine_configs/gemma3/vswa_decode.yaml
./launch/disagg_router.sh