---
id: pr-tensorrt-llm-12946
repo: NVIDIA/TensorRT-LLM
pr: 12946
title: '[#12784][feat] AutoDeploy: Optimize DeepSeek-R1 model performance'
author: taylor-yb-lee
date: '2026-05-08'
url: https://github.com/NVIDIA/TensorRT-LLM/pull/12946
source_category: upstream-code
architectures:
- sm100
tags:
- MLA
- allreduce
- benchmark_test
- blackwell
- gemm_quant
- norm_elementwise
techniques:
- fine-grained-quantization
- kernel-fusion
hardware_features:
- blackwell
kernel_types:
- gemm
- mla
- moe
- norm
languages:
- triton
captured_at: '2026-05-16'
status: merged
merge_sha: 6cd23bccdf1e5c072a693dc47d45abb79e1d9b23
inclusion_reason: Look for fusion, vectorization, dtype conversion, and store-traffic reductions before changing math.
changed_paths:
- examples/auto_deploy/model_registry/configs/deepseek-r1.yaml
- tensorrt_llm/_torch/auto_deploy/config/default.yaml
- tensorrt_llm/_torch/auto_deploy/custom_ops/distributed/trtllm_dist.py
- tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/trtllm_moe.py
- tensorrt_llm/_torch/auto_deploy/custom_ops/linear/swiglu.py
- tensorrt_llm/_torch/auto_deploy/custom_ops/quantization/__init__.py
- tensorrt_llm/_torch/auto_deploy/custom_ops/quantization/torch_quant.py
- tensorrt_llm/_torch/auto_deploy/custom_ops/quantization/trtllm_quant.py
- tensorrt_llm/_torch/auto_deploy/llm_args.py
- tensorrt_llm/_torch/auto_deploy/mlir/codegen/triton_emitter.py
- tensorrt_llm/_torch/auto_deploy/mlir/dialect.py
- tensorrt_llm/_torch/auto_deploy/mlir/fusion/subgraph_discovery.py
- tensorrt_llm/_torch/auto_deploy/mlir/fx_to_mlir.py
- tensorrt_llm/_torch/auto_deploy/mlir/mlir_to_fx.py
- tensorrt_llm/_torch/auto_deploy/models/custom/modeling_deepseek.py
- tensorrt_llm/_torch/auto_deploy/models/custom/modeling_deepseek_ir.py
- tensorrt_llm/_torch/auto_deploy/transform/library/fuse_quant.py
- tensorrt_llm/_torch/auto_deploy/transform/library/fusion.py
- tensorrt_llm/_torch/auto_deploy/transform/library/mlir_elementwise_fusion.py
- tensorrt_llm/_torch/auto_deploy/transform/library/multi_stream_gemm.py
- tensorrt_llm/_torch/auto_deploy/transform/library/quantization.py
- tensorrt_llm/_torch/auto_deploy/transform/library/rms_norm.py
- tensorrt_llm/_torch/auto_deploy/transform/library/cleanup_identity_dtype_cast.py
- tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py
- tensorrt_llm/_torch/auto_deploy/transform/library/fuse_silu_mul.py
- tensorrt_llm/_torch/auto_deploy/transform/library/fuse_swiglu.py
artifact_dir: evidence/pull-bundles/tensorrt-llm/gh-12946
head_ref_oid: 6fe67471b1b5a9a067ef8e744fd1eb8ec8ee5d29
---
# [#12784][feat] AutoDeploy: Optimize DeepSeek-R1 model performance

- Repository: `NVIDIA/TensorRT-LLM`
- PR: [12946](https://github.com/NVIDIA/TensorRT-LLM/pull/12946)
- Decision: `Look for fusion, vectorization, dtype conversion, and store-traffic reductions before changing math.`
- Reason: Look for fusion, vectorization, dtype conversion, and store-traffic reductions before changing math.

## Changed Paths

- `examples/auto_deploy/model_registry/configs/deepseek-r1.yaml`
- `tensorrt_llm/_torch/auto_deploy/config/default.yaml`
- `tensorrt_llm/_torch/auto_deploy/custom_ops/distributed/trtllm_dist.py`
- `tensorrt_llm/_torch/auto_deploy/custom_ops/fused_moe/trtllm_moe.py`
- `tensorrt_llm/_torch/auto_deploy/custom_ops/linear/swiglu.py`
- `tensorrt_llm/_torch/auto_deploy/custom_ops/quantization/__init__.py`
- `tensorrt_llm/_torch/auto_deploy/custom_ops/quantization/torch_quant.py`
- `tensorrt_llm/_torch/auto_deploy/custom_ops/quantization/trtllm_quant.py`
- `tensorrt_llm/_torch/auto_deploy/llm_args.py`
- `tensorrt_llm/_torch/auto_deploy/mlir/codegen/triton_emitter.py`
- `tensorrt_llm/_torch/auto_deploy/mlir/dialect.py`
- `tensorrt_llm/_torch/auto_deploy/mlir/fusion/subgraph_discovery.py`
- `tensorrt_llm/_torch/auto_deploy/mlir/fx_to_mlir.py`
- `tensorrt_llm/_torch/auto_deploy/mlir/mlir_to_fx.py`
- `tensorrt_llm/_torch/auto_deploy/models/custom/modeling_deepseek.py`
- `tensorrt_llm/_torch/auto_deploy/models/custom/modeling_deepseek_ir.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/fuse_quant.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/fusion.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/mlir_elementwise_fusion.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/multi_stream_gemm.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/quantization.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/rms_norm.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/cleanup_identity_dtype_cast.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/collectives.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/fuse_silu_mul.py`
- `tensorrt_llm/_torch/auto_deploy/transform/library/fuse_swiglu.py`

## Evidence Bundle

`evidence/pull-bundles/tensorrt-llm/gh-12946`
