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Paper Citation Record · LEDGER

LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2403.06504.

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

pith.paper-citation-record.v1
2403.06504 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:10:15.702699Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T10:19:59.839971Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 9347a12c-dc38-4126-87ec-492546f5dec0 · inbound

Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage cites this paper.

Cost-Efficient LLM Training with Lifetime-Aware Tensor Offloading via GPUDirect Storage LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:32.342788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:00:32.342788Z digest=sha256:1b8d9b8b79cf4565bde033df365d34012db1d6b2126eb3fdc44754edef94b1e5

Observation fbf75f83-4a47-4bff-87af-6be3b3d6d34b · inbound

Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System cites this paper.

Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-05T19:30:24.199300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:30:24.199300Z digest=sha256:afe15e74b00c2261e0f74f305a2ea4711b453b7f6a71c180c1153ec87659d8b8

Observation ae8bfc6c-65c6-4abc-a016-282111a76e94 · inbound

MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall cites this paper.

MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T11:38:39.507558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:38:39.507558Z digest=sha256:b1549e9eea52d89edd7649ebf01cf78e14d7de387d9afdf5823a0026a079bdd5

Observation 197644b6-771a-4842-bdcd-3b177d0ea4de · inbound

XBOF: A Cost-Efficient CXL JBOF with Inter-SSD Compute Resource Sharing cites this paper.

XBOF: A Cost-Efficient CXL JBOF with Inter-SSD Compute Resource Sharing LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-15T16:10:15.702699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:10:15.702699Z digest=sha256:ef2874b667ffea34cf4896fc99bbdb76f67d18b72c75b3066432662ae2993d4c

Observation 922ed063-0bef-483d-9fab-166ecbfb377b · inbound

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU cites this paper.

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-13T23:47:39.219131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:47:39.219131Z digest=sha256:989ab206b69510d10dabfb421da00bb0f7e0be08e10997e0a31a553f2c3e4d2f

Observation 3a09c57e-15fa-4eea-b333-08beb65365d1 · inbound

Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains cites this paper.

Flowr -- Scaling Up Retail Supply Chain Operations Through Agentic AI in Large Scale Supermarket Chains LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:55:49.191901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T19:29:30.436621Z digest=sha256:0cb4fd1bfb336f2dce9c6ed064e0aa79a4404836e09e36ad01cc079bb724ce7a

Observation 2aaca9df-63b7-4828-9d9b-82a4246ba323 · inbound

Efficient Training on Multiple Consumer GPUs with RoundPipe cites this paper.

Efficient Training on Multiple Consumer GPUs with RoundPipe LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:31:26.811674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-07T10:37:22.251566Z digest=sha256:19f1756fd621d4da06650b899fc485d05fd424e7535061bcc653ca13f62bf338

Observation ac1498c0-bcfe-4b13-aa53-39cda53d554b · inbound

Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments cites this paper.

Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-21T10:19:59.842208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-21T10:17:29.611062Z digest=sha256:c5c1a3d7e8ddbf7058b5a57f8fa769f76b161dee0b2d587b836994aacc74eaba