Speedup
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Speedup has 15 facts recorded in Dontopedia across 11 references, with 1 live disagreement.
Mostly:at tokens per second(2), is(1), prioritized metric(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (4)
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.
causesLargeConstantFactorCauses Large Constant Factor(1)
- Python Mlx Overhead
ex:python-mlx-overhead
demonstratesDemonstrates(1)
- Benchmark
ex:benchmark
exhibitsLargerSpeedupGapExhibits Larger Speedup Gap(1)
- Full 12 Layer 768d Model
ex:full-12-layer-768d-model
purposePurpose(1)
- Parallel Processing Implementation
ex:parallel-processing-implementation
Other facts (15)
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.
| Predicate | Value | Ref |
|---|---|---|
| At Tokens Per Second | 32M tok/s | [8] |
| At Tokens Per Second | 81M tok/s | [8] |
| Is | 1.7 | [1] |
| Prioritized Metric | It Per S | [2] |
| Is Desirable | null | [3] |
| Is6x Faster | True | [4] |
| Increases With Scale | Weak Scaling | [5] |
| Trades Off | Storage | [6] |
| Initially Estimated As | 10-30x | [7] |
| Matches Theory | 3.5× on 37% of forward = 1.42× ceiling | [9] |
| Confirmed by | Theory | [9] |
| Satisfies | close | [9] |
| Close to | Theory Ceiling | [9] |
| Was Massive | null | [10] |
| Rdf:type | Performance Goal | [11] |
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.
References (11)
ctx:discord/blah/watt-activation/part-15ctx:discord/blah/watt-activation/part-63ctx:discord/blah/watt-activation/part-490ctx:discord/blah/watt-activation/part-501ctx:discord/blah/watt-activation/part-527ctx:discord/blah/watt-activation/part-590ctx:discord/blah/watt-activation/part-587ctx:discord/blah/watt-activation/part-692ctx:discord/blah/watt-activation/part-698ctx:discord/blah/watt-activation/part-325ctx:claims/beam/dd276301-ccba-4bf0-8c83-855e2c5ddb6c- full textbeam-chunktext/plain1 KB
doc:beam/dd276301-ccba-4bf0-8c83-855e2c5ddb6cShow excerpt
# Implement secure tuning logic here return np.random.rand(len(dataset)) # Apply secure tuning to datasets tuned_datasets = [secure_tuning(dataset) for dataset in datasets] # Calculate compliance rate compliance_rate = np.mean([np…
See also
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