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ARXIV:2604.01621 · LLM INFERENCE OPTIMIZATION · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01621LLM INFERENCE OPTIMIZATIONSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEWanqian Li · Jintao Peng · Zongfei Jing · Tianyu Zhang · Ze Long · Xianjie Qiao · +4 at arXiv
A novel inference parallelization strategy for LLMs that improves performance by offloading MoE weights and enabling independent GPU execution.
Opportunity summary
Pain A novel inference parallelization strategy for LLMs that improves performance by offloading MoE weights and enabling independent GPU execution.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A novel inference parallelization strategy for LLMs that improves performance by offloading MoE weights and enabling independent GPU execution. We present DWDP (Distributed Weight Data Parallelism), an inference parallelization strategy that preserves data-parallel execution…
Large language model (LLM) inference increasingly depends on multi-GPU execution, yet existing inference parallelization strategies require layer-wise inter-rank synchronization, making end-to-end performance sensitive to workload imbalance. We present DWDP (Distributed Weight Data Parallelism), an…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Implemented in TensorRT-LLM and evaluated with DeepSeek-R1 on GB200 NVL72, DWDP improves end-to-end output TPS/GPU by 8.8% at comparable TPS/user in the 20-100 TPS/user…
LLM Inference Optimization moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A novel inference parallelization strategy for LLMs that improves performance by offloading MoE weights and enabling independent GPU execution.
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10.48550/arXiv.2604.01621A novel inference parallelization strategy for LLMs that improves performance by offloading MoE weights and enabling independent GPU execution.
Abstract
Large language model (LLM) inference increasingly depends on multi-GPU execution, yet existing inference parallelization strategies require layer-wise inter-rank synchronization, making end-to-end performance sensitive to workload imbalance. We present DWDP (Distributed Weight Data Parallelism), an inference parallelization strategy that preserves data-parallel execution while offloading MoE weights across peer GPUs and fetching missing experts on demand. By removing collective inter-rank synchronization, DWDP allows each GPU to progress independently. We further address the practical overheads of this design with two optimizations for split-weight management and asynchronous remote-weight prefetch. Implemented in TensorRT-LLM and evaluated with DeepSeek-R1 on GB200 NVL72, DWDP improves end-to-end output TPS/GPU by 8.8% at comparable TPS/user in the 20-100 TPS/user serving range under 8K input sequence length and 1K output sequence length.
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Proof status
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What was readable
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Dimensions overall score 4.0
PROBLEM
A novel inference parallelization strategy for LLMs that improves performance by offloading MoE weights and enabling independent GPU execution. We present DWDP (Distributed Weight Data Parallelism), an inference parallelization strategy that preserves data-parallel execution whi...
METHOD
Large language model (LLM) inference increasingly depends on multi-GPU execution, yet existing inference parallelization strategies require layer-wise inter-rank synchronization, making end-to-end performance sensitive to workload imbalance. We present DWDP (Distributed Weight D...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Implemented in TensorRT-LLM and evaluated with DeepSeek-R1 on GB200 NVL72, DWDP improves end-to-end output TPS/GPU by 8.8% at comparable TPS/user in the 20-100 TPS/user serving range under 8K input sequen...
WHY NOW
LLM Inference Optimization moved forward this cycle; last verified April 2026. Public score 4.0/10.
DWDP improves end-to-end output TPS/GPU by 8.8% at comparable TPS/user in the 20-100 TPS/user serving range under 8K input sequence length and 1K output sequence length.
Directly stated in abstract with specific numeric improvement percentage and test conditions
partial
By removing collective inter-rank synchronization, DWDP allows each GPU to progress independently.
Directly stated in abstract as a key feature of the method
partial
existing inference parallelization strategies require layer-wise inter-rank synchronization, making end-to-end performance sensitive to workload imbalance.
Directly stated in abstract as motivation for the work
partial
DWDP (Distributed Weight Data Parallelism), an inference parallelization strategy that preserves data-parallel execution while offloading MoE weights across peer GPUs and fetching missing experts on demand.
Directly stated in abstract describing the core method
partial
We further address the practical overheads of this design with two optimizations for split-weight management and asynchronous remote-weight prefetch.
Directly stated in abstract describing implementation details
partial
Implemented in TensorRT-LLM and evaluated with DeepSeek-R1 on GB200 NVL72
Directly stated in abstract with specific implementation and evaluation details
partial
Large language model (LLM) inference increasingly depends on multi-GPU execution
Directly stated in abstract as context for the work
partial
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A novel inference parallelization strategy for LLMs that improves performance by offloading MoE weights and enabling independent GPU execution.
Segment
LLM Inference Optimization
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Commercial read
4.0/10 public viability
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