Dontopedia

Gradient Checkpointing

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)

Gradient Checkpointing has 27 facts recorded in Dontopedia across 5 references.

27 facts·26 predicates·5 sources

Mostly:is purely memory optimization(2), not worth it at five point six million params(1), causes slightly more complex debugging(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (9)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

associatedWithAssociated With(1)

enabledFeatureEnabled Feature(1)

implicatureRequiresMoreMemoryImplicature Requires More Memory(1)

isSlowerDueToExtraForwardPassesIs Slower Due to Extra Forward Passes(1)

performsExplanationPerforms Explanation(1)

recommendsConditionalUseRecommends Conditional Use(1)

requiresGradientCheckpointingToFitRequires Gradient Checkpointing to Fit(1)

settingSetting(1)

slightlyWorseWithCheckpointingSlightly Worse With Checkpointing(1)

Other facts (27)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

27 facts
PredicateValueRef
Is Purely Memory Optimizationtrue[1]
Is Purely Memory Optimizationtrue[3]
Not Worth It at Five Point Six Million Params5 6m Params Model[1]
Causes Slightly More Complex DebuggingDebugging[1]
Teleologically Optimizes MemoryMemory[1]
Evaluated As Not Worth It Small Models5 6m Params Model[1]
Guaranteed Same ConvergenceConvergence[1]
Has No Impact on Model QualityModel Quality[1]
Sweet Spot Is Turn on When Need MemorySweet Spot[1]
Produces Same Everythingtrue[1]
Presupposes Py Torch UsagePytorch Autograd Machinery[1]
Causes Thirty Percent Slower Training~30%[1]
Trades Memory for Computenull[2]
Increases Compute Usage~30%[2]
Is Teleologically for Memory Savingnull[2]
Reduces Activation Memory~60%[2]
Rdf:typeOptimization Technique[3]
Results in Approximate Training Slowdown0.3[3]
Causes Training Slowdowntrue[3]
Impacts Model Qualityfalse[3]
Produces Mathematically Identical Gradientstrue[3]
Has Same Convergencetrue[3]
Has Same Losstrue[3]
Makes Debugging Slightly More Complextrue[3]
Saves Approximately60% activation memory[4]
Memory Impact60 Percent Less Activation Memory[5]
Compute Impact30 Percent More Compute[5]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

27 undated
—
isPurelyMemoryOptimizationblah/training-and-evals/part-30
true
—
notWorthItAtFivePointSixMillionParamsblah/training-and-evals/part-30
ex:5-6m-params-model
—
causesSlightlyMoreComplexDebuggingblah/training-and-evals/part-30
ex:debugging
—
teleologicallyOptimizesMemoryblah/training-and-evals/part-30
ex:memory
—
evaluatedAsNotWorthItSmallModelsblah/training-and-evals/part-30
ex:5-6m-params-model
—
guaranteedSameConvergenceblah/training-and-evals/part-30
ex:convergence
—
hasNoImpactOnModelQualityblah/training-and-evals/part-30
ex:model-quality
—
sweetSpotIsTurnOnWhenNeedMemoryblah/training-and-evals/part-30
ex:sweet-spot
—
producesSameEverythingblah/training-and-evals/part-30
true
—
presupposesPyTorchUsageblah/training-and-evals/part-30
ex:pytorch-autograd-machinery
—
causesThirtyPercentSlowerTrainingblah/training-and-evals/part-30
~30%
—
tradesMemoryForComputeblah/watt-activation/part-13
null
—
increasesComputeUsageblah/watt-activation/part-13
~30%
—
isTeleologicallyForMemorySavingblah/watt-activation/part-13
null
—
reducesActivationMemoryblah/watt-activation/part-13
~60%
—
typeblah/training-and-evals/30
ex:OptimizationTechnique
—
resultsInApproximateTrainingSlowdownblah/training-and-evals/30
0.3
—
causesTrainingSlowdownblah/training-and-evals/30
true
—
impactsModelQualityblah/training-and-evals/30
false
—
producesMathematicallyIdenticalGradientsblah/training-and-evals/30
true
—
hasSameConvergenceblah/training-and-evals/30
true
—
hasSameLossblah/training-and-evals/30
true
—
isPurelyMemoryOptimizationblah/training-and-evals/30
true
—
makesDebuggingSlightlyMoreComplexblah/training-and-evals/30
true
—
savesApproximatelyblah/watt-activation/14
60% activation memory
—
memoryImpactblah/watt-activation/12
ex:60-percent-less-activation-memory
—
computeImpactblah/watt-activation/12
ex:30-percent-more-compute

References (5)

5 references
  1. [1]Part 3011 facts
    ctx:discord/blah/training-and-evals/part-30
  2. [2]Part 134 facts
    ctx:discord/blah/watt-activation/part-13
  3. [3]309 facts
    ctx:discord/blah/training-and-evals/30
    • full texttraining-and-evals-30
      text/plain1 KBdoc:agent/training-and-evals-30/52f8dc95-d5eb-428a-aadb-9a96b0cd0b06
      Show excerpt
      [2026-02-26 20:08] xenonfun: ``` The tradeoff is compute for memory: 1. ~30% slower training — Each checkpointed block's forward pass runs twice: once during forward, once recomputed during backward (since we discarded the intermediates)…
  4. [4]141 fact
    ctx:discord/blah/watt-activation/14
    • full textwatt-activation-14
      text/plain3 KBdoc:agent/watt-activation-14/0df6b72b-455d-4cdc-b6f5-fc3dcafab793
      Show excerpt
      [2026-02-28 20:19] xenonfun: okay finally, tokenizing always seems a bitch in colab then running not so bad, 8x quicker: ``` 2026-02-28 20:17:26,467 [INFO] numexpr.utils: NumExpr defaulting to 12 threads. Loading fused model from cross_spec…
  5. [5]122 facts
    ctx:discord/blah/watt-activation/12
    • full textwatt-activation-12
      text/plain3 KBdoc:agent/watt-activation-12/2b226561-3075-47ab-89b3-591d7663c93b
      Show excerpt
      [2026-02-27 14:42] xenonfun: the codebase already computes SVD in model.py:effective_rank (files: Screenshot_2026-02-27_at_9.41.31_AM.png) [2026-02-27 15:41] xenonfun: (files: Screenshot_2026-02-27_at_10.41.22_AM.png) [2026-02-27 15:44] xe…

See also

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