Paper Citation Record · LEDGER

ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2206.01861.

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

pith.paper-citation-record.v1
2206.01861 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:38:22.309181Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:16:16.046751Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5d495910-c8b9-4bfa-b361-25c92364875a · inbound

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 171

Resolution: verified exact; arxiv_id, observed 2026-05-13T13:35:36.185243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 03b4f879-9892-4bcb-abf0-9017dbf482d2 · inbound

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers cites this paper.

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T17:18:35.274980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b7bbd66-67a3-49f0-b4ba-f25606e2f5ac · inbound

Accelerating Large Language Model Decoding with Speculative Sampling cites this paper.

Accelerating Large Language Model Decoding with Speculative Sampling ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:29:36.307343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6589bb8-472a-4d55-b1fb-2a71b309c258 · inbound

QLoRA: Efficient Finetuning of Quantized LLMs cites this paper.

QLoRA: Efficient Finetuning of Quantized LLMs ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:29:53.790343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 419f3a01-d77c-4b52-9378-02df1fff1d21 · inbound

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration cites this paper.

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:29:11.224966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29d83d7a-d3a3-4d48-b1df-85a29bb200fd · inbound

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models cites this paper.

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T18:00:50.460651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 405b02e5-3090-4efd-b4dc-2c5bd61b737b · inbound

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration cites this paper.

BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T18:18:05.575588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9015d2e5-5189-4571-abb4-2871b0e365e5 · inbound

Pushing the Limits of Large Language Model Quantization via the Linearity Theorem cites this paper.

Pushing the Limits of Large Language Model Quantization via the Linearity Theorem ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T12:07:30.258416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cc0df57-4fc6-4af5-9030-8a7ae42ce0c6 · inbound

4bit-Quantization in Vector-Embedding for RAG cites this paper.

4bit-Quantization in Vector-Embedding for RAG ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T19:12:58.837909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c187c5-2f9a-4c8e-b164-77642734c8de · inbound

OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting cites this paper.

OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T16:13:19.988135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a49b61a-c3c2-46dd-a9a7-c8c2c44b479b · inbound

Compute-Optimal LLMs Provably Generalize Better With Scale cites this paper.

Compute-Optimal LLMs Provably Generalize Better With Scale ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T11:38:22.309181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45ec0f7b-73cb-4e47-bc37-5d2b3f5d44d3 · inbound

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference cites this paper.

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T21:55:07.877611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a138f024-ee03-4bc0-97c7-7b1d9e2e4d83 · inbound

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling cites this paper.

PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:42:41.045802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8be7de8b-4369-49d6-98ba-720147f5f4b1 · inbound

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models cites this paper.

ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:29.118203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 934d023b-00d6-45e5-a64e-f1d9d38e76e3 · inbound

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study cites this paper.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:47.848505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3f54d90-0c07-462f-b783-f990656e6967 · inbound

Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models cites this paper.

Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T17:31:08.335975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 38c47fdd-5b79-4043-b192-f7df2b5a3817 · inbound

A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models cites this paper.

A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T14:25:29.242563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3d0d9e1-e4d3-4c83-882e-1e7635f2e040 · inbound

Motion-Compensated Weight Compression cites this paper.

Motion-Compensated Weight Compression ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-06-30T13:04:40.440211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2687967a-64b3-4698-8ad1-f18a8546b249 · inbound

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation cites this paper.

Alignment Collapse Under KV Cache Quantization: Diagnosis and Mitigation ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:16:16.048491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6276d88e-1bee-4694-b241-3aaf305918bf · inbound

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights cites this paper.

CubicQuant: Parametric Non-Uniform Codebooks for High-Throughput LLM Inference with 1-8-Bit Weights ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T14:37:51.939462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d81b60d8-44d9-4fd9-a26c-ae746f334db4 · inbound

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra cites this paper.

Spec Sheets Are Not Kernels: An ISA- and Source-Level Audit of INT8 Availability on NVIDIA Blackwell Ultra ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T00:35:55.878782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.