Paper Citation Record · LEDGER

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors

As of 19 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2412.19829.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.19829 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:54:48.416801Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f36af186-816b-470d-815b-2f81a2963f5f · outbound

This paper cites Attention is all you need,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Attention is all you need,

Reference 1

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Observation 8bd85eaa-6025-4821-b7fe-fb18b71fb206 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T11:54:48.250554Z

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Observation cb5c75a6-5332-414c-8063-1e4e3b29db00 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

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Observation 5bf5e183-3f43-4d1a-b7c3-e34da2bb1b8a · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Transformers are rnns: Fast autoregressive transformers with linear attention,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.929197Z

Source-reported events for the cited work

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Observation fc0b4dc0-1e0d-4bad-9e86-e50d40b83436 · outbound

This paper cites Habana labs purpose-built ai inference and training processor architectures: Scaling ai training systems using standard ethernet with gaudi processor,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Habana labs purpose-built ai inference and training processor architectures: Scaling ai training systems using standard ethernet with gaudi processor,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.913675Z

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Observation c959769b-4f7a-49d9-91f2-dc3b4ee9f67c · outbound

This paper cites Accelerating scien- tific applications with sambanova reconfigurable dataflow architecture,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Accelerating scien- tific applications with sambanova reconfigurable dataflow architecture,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.898501Z

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Observation 1e539706-d3e7-4744-bb61-068b4defd62f · outbound

This paper cites Cerebras architecture deep dive: First look inside the hw/sw co- design for deep learning: Cerebras systems,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Cerebras architecture deep dive: First look inside the hw/sw co- design for deep learning: Cerebras systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.883395Z

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Observation 48cbac8e-90fb-4032-bec9-31688be9c06b · outbound

This paper cites Benchmarking and in-depth performance study of large language models on habana gaudi processors,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Benchmarking and in-depth performance study of large language models on habana gaudi processors,

Reference 8

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Observation e05b5681-1467-4518-98fa-fee124484454 · outbound

This paper cites Reformer: The Efficient Transformer.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Reformer: The Efficient Transformer

Reference 9

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Source-reported events for the cited work

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Observation 72856122-5b70-4728-920a-b7858da22ee7 · outbound

This paper cites Big bird: Transformers for longer sequences,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Big bird: Transformers for longer sequences,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.854434Z

Source-reported events for the cited work

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Observation e51952b4-5603-45a9-a74c-1ac378dbf6b6 · outbound

This paper cites Kernel methods in machine learning,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Kernel methods in machine learning,

Reference 11

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Source-reported events for the cited work

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Observation 93e61b90-2443-45c5-932f-5b8ff3d18e85 · outbound

This paper cites Rethinking Attention with Performers.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Rethinking Attention with Performers

Reference 12

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Observation f76a66df-d715-4bbd-9ccf-a6bc344a3af8 · outbound

This paper cites Scat- terbrain: Unifying sparse and low-rank attention,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Scat- terbrain: Unifying sparse and low-rank attention,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.828075Z

Source-reported events for the cited work

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Observation b290e1ee-4f7d-440f-9d5f-5813107bc8bb · outbound

This paper cites Improving language understanding by generative pre-training,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Improving language understanding by generative pre-training,

Reference 14

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Observation 83862bfe-4f3b-4bb2-b219-02e6976878b0 · outbound

This paper cites Longformer: The Long-Document Transformer.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Longformer: The Long-Document Transformer

Reference 15

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Observation 09d976db-3f85-4f9a-84d2-87fb6676bc5f · outbound

This paper cites Gaudi training platform white paper,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Gaudi training platform white paper,

Reference 16

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Observation 46dd2976-7781-4fd7-bcca-18d59c0ca953 · outbound

This paper cites Tpc programming.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Tpc programming

Reference 17

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Observation 23758d21-9ad7-40c8-8487-db4d3a9de4ff · outbound

This paper cites Language Models are Few-Shot Learners.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Language Models are Few-Shot Learners

Reference 18

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Source-reported events for the cited work

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Observation 7ebbf9c9-3166-4fe1-a5b5-f6fb5a02d64c · outbound

This paper cites Cost and utilization optimization of amazon ec2 instances,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Cost and utilization optimization of amazon ec2 instances,

Reference 19

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Observation 9fd46263-85b1-451d-b96a-02e7c08ae4c8 · outbound

This paper cites GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow,

Reference 20

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Observation 99e1574e-96ba-47dc-ae03-94043fafcf2e · outbound

This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 21

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Observation c3e6569f-7a2c-4927-a88d-c7ec6cb8a572 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 22

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This paper cites Visual transformers: Token- based image representation and processing for computer vision,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Visual transformers: Token- based image representation and processing for computer vision,

Reference 23

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This paper cites Pointer sentinel mixture models,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Pointer sentinel mixture models,

Reference 24

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Observation ec6b31ce-9705-4261-9750-32a000ac2fbd · outbound

This paper cites Aligning books and movies: Towards story-like visual explanations by watching movies and reading books,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Aligning books and movies: Towards story-like visual explanations by watching movies and reading books,

Reference 25

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Observation ab2c7431-80c8-425c-9145-5b7c46d6dab0 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 26

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This paper cites Imagenet: A large-scale hierarchical image database,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Imagenet: A large-scale hierarchical image database,

Reference 27

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This paper cites Bridges-2: A platform for rapidly-evolving and data intensive research,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Bridges-2: A platform for rapidly-evolving and data intensive research,

Reference 28

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Observation a2eecb08-6940-4cff-941c-0e5ad969f36f · outbound

This paper cites Dota: detect and omit weak attentions for scalable transformer acceleration,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Dota: detect and omit weak attentions for scalable transformer acceleration,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.666276Z

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This paper cites Vitality: Unifying low-rank and sparse approximation for vision transformer acceleration with a linear taylor attention,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Vitality: Unifying low-rank and sparse approximation for vision transformer acceleration with a linear taylor attention,

Reference 30

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This paper cites An algorithm–hardware co-optimized framework for accelerating n: M sparse transformers,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors An algorithm–hardware co-optimized framework for accelerating n: M sparse transformers,

Reference 31

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Observation efa31894-8e24-4a1e-82b4-776cb70fb868 · outbound

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GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Boost vision transformer with gpu- friendly sparsity and quantization,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:54:48.614495Z

Source-reported events for the cited work

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Observation ee428756-dc2a-45b9-b9f0-289779c900e9 · outbound

This paper cites Transformer Acceleration with Dynamic Sparse Attention.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Transformer Acceleration with Dynamic Sparse Attention

Reference 33

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Observation a37b3c6c-a1db-4e92-97e1-4a79a1269713 · outbound

This paper cites An fpga-based transformer accelerator using output block stationary dataflow for object recognition applications,.

GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors An fpga-based transformer accelerator using output block stationary dataflow for object recognition applications,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T11:54:48.416801Z

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GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:54:48.744684Z

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Pith citing papers

No inbound Pith citation observations are available.