DeepSpeech: wrong Inference results in DeepSpeech. CPU vs GPU?

Inference Running on GPU: Linux Ubuntu 16.04 name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.43 totalMemory: 3.94GiB CUDA/cuDNN version:9.0

Inference Running on CPU: Linux Ubuntu 16.04 i5 processor. 8 GB RAM

CPU gives Correct expected Inference result. like this,

deepspeech --model output_graph.pbmm --alphabet models/alphabet.txt --lm models/lm.binary --trie models/trie --audio 1.wav
Loading model from file output_graph.pbmm
TensorFlow: v1.11.0-9-g97d851f
DeepSpeech: v0.3.0-0-gef6b5bd
Loaded model in 0.00495s.
Loading language model from files models/lm.binary models/trie
Loaded language model in 1.5s.
Running inference.
**a what is the benefit for pan card** 
Inference took 3.080s for 4.598s audio file.

but in GPU gives wrong inference for same model. like this,

deepspeech --model model_export_cv02/output_graph.pbmm --alphabet models/alphabet.txt --lm models/lm.binary --trie models/trie --audio 1.wav
Loading model from file model_export_cv02/output_graph.pbmm
TensorFlow: v1.11.0-9-g97d851f
DeepSpeech: v0.3.0-0-gef6b5bd
Loaded model in 0.304s.
Loading language model from files models/lm.binary models/trie
Loaded language model in 1.35s.
Running inference.
**e e love jesting is wicebanethexapsing and weareiosdd** 
Inference took 3.154s for 10.000s audio file.

sir, My model trained on, EC2 P3 Instance 512GB RAM, 8 GPUs 16GB.

sir what is the problem? i don’t know have idea, how to resolve this?

trained on: checkout DeepSpeech v0.3.0 and pretrained model, lm, trie.

how to resolve this issue sir. thanks sir.