nbits
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
nbits is Number of bits per sub-quantizer.
23 facts·15 predicates·7 sources·2 in dispute
Mostly:rdf:type(5), ex:description(1), ex:value(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (10)
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.
appliesToApplies to(2)
- Accuracy Memory Tradeoff
ex:accuracy-memory-tradeoff - Parameter Tradeoff Pattern
ex:parameter-tradeoff-pattern
hasParameterHas Parameter(2)
- Index Ivfpq
ex:IndexIVFPQ - Index Ivfpq
ex:IndexIVFPQ
applies-parameterApplies Parameter(1)
- Efficient Indexing Methods
ex:efficient-indexing-methods
consists-ofConsists of(1)
- Ivf Pq Components
ex:ivf-pq-components
discussesParameterDiscusses Parameter(1)
- Parameter Tuning
ex:parameter-tuning
ex:requiresEx:requires(1)
- Index Ivfpq
ex:IndexIVFPQ
involves-adjustingInvolves Adjusting(1)
- Parameter Tuning
ex:parameter-tuning
requiresParameterTuningRequires Parameter Tuning(1)
- Ivfpq
IVFPQ
Other facts (19)
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.
19 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Bits Per Subquantizer | [2] |
| Rdf:type | Index Parameter | [3] |
| Rdf:type | Parameter | [5] |
| Rdf:type | Tuning Parameter | [6] |
| Rdf:type | Index Parameter | [7] |
| Ex:description | Number of bits per subquantizer | [1] |
| Ex:value | 8 | [1] |
| Ex:typical Range | Variable Bits | [1] |
| Description | Number of bits per sub-quantizer | [5] |
| Recommended Value | 8 | [5] |
| Affects | Balance Speed Accuracy | [5] |
| Controls Bit Depth | true | [5] |
| Describes | Number of Bits Per Sub Quantizer | [6] |
| Described As | Number of bits per sub-quantizer | [7] |
| Value | 8 | [7] |
| Higher Value Effect | improve accuracy | [7] |
| Higher Value Cost | increases memory usage | [7] |
| Trade Off | accuracy-vs-memory | [7] |
| Abbreviation | nbits | [7] |
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.
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descriptionbeam/9f354551-a9f5-474b-a587-082e952c4a41
Number of bits per subquantizer
—
valuebeam/9f354551-a9f5-474b-a587-082e952c4a41
8
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typicalRangebeam/9f354551-a9f5-474b-a587-082e952c4a41
ex:variable-bits
—
typebeam/276709e4-43dc-4dfa-a983-c23bf40e789f
ex:bits-per-subquantizer
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typebeam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
ex:IndexParameter
—
labelbeam/5322bb97-5c91-4db0-bf82-cf4a4ac41105
nbits parameter
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labelbeam/5b048fde-0e90-41b4-bd79-29398c7ac010
nbits
—
typebeam/9aef4a43-c110-4730-bed6-18e6312b77ad
ex:Parameter
—
labelbeam/9aef4a43-c110-4730-bed6-18e6312b77ad
nbits
—
descriptionbeam/9aef4a43-c110-4730-bed6-18e6312b77ad
Number of bits per sub-quantizer
—
recommended-valuebeam/9aef4a43-c110-4730-bed6-18e6312b77ad
8
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affectsbeam/9aef4a43-c110-4730-bed6-18e6312b77ad
ex:balance-speed-accuracy
—
controls-bit-depthbeam/9aef4a43-c110-4730-bed6-18e6312b77ad
true
—
typebeam/deee8e59-885e-45e2-98e2-b079298375cc
ex:TuningParameter
—
describesbeam/deee8e59-885e-45e2-98e2-b079298375cc
ex:number-of-bits-per-sub-quantizer
—
labelbeam/deee8e59-885e-45e2-98e2-b079298375cc
nbits
—
typebeam/8c21f541-c703-4998-aae0-19638ef54326
ex:IndexParameter
—
describedAsbeam/8c21f541-c703-4998-aae0-19638ef54326
Number of bits per sub-quantizer
—
valuebeam/8c21f541-c703-4998-aae0-19638ef54326
8
—
higherValueEffectbeam/8c21f541-c703-4998-aae0-19638ef54326
improve accuracy
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higherValueCostbeam/8c21f541-c703-4998-aae0-19638ef54326
increases memory usage
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tradeOffbeam/8c21f541-c703-4998-aae0-19638ef54326
accuracy-vs-memory
—
abbreviationbeam/8c21f541-c703-4998-aae0-19638ef54326
nbits
References (7)
7 references
ctx:claims/beam/9f354551-a9f5-474b-a587-082e952c4a41- full textbeam-chunktext/plain1 KB
doc:beam/9f354551-a9f5-474b-a587-082e952c4a41Show excerpt
faiss.omp_set_num_threads(4) # Adjust based on your system's capabilities # Create an IVFFlat index quantizer = faiss.IndexFlatL2(128) index = faiss.IndexIVFFlat(quantizer, 128, nlist, faiss.METRIC_L2) # Train the index index.train(vecto…
ctx:claims/beam/276709e4-43dc-4dfa-a983-c23bf40e789f- full textbeam-chunktext/plain1 KB
doc:beam/276709e4-43dc-4dfa-a983-c23bf40e789fShow excerpt
- Try different values for `nlist` and `nprobe` to find the optimal balance between speed and accuracy. - For example, you might try `nlist = 200` and `nprobe = 5` or `nprobe = 20`. 2. **Monitor Performance**: - Use `time` or `cPr…
ctx:claims/beam/5322bb97-5c91-4db0-bf82-cf4a4ac41105- full textbeam-chunktext/plain1 KB
doc:beam/5322bb97-5c91-4db0-bf82-cf4a4ac41105Show excerpt
- For larger datasets (millions or more vectors), IVFPQ or HNSW are often better choices due to their efficiency in terms of memory and search speed. 2. **Search Latency Requirements**: - If you need very low search latency (under 20…
ctx:claims/beam/5b048fde-0e90-41b4-bd79-29398c7ac010- full textbeam-chunktext/plain1 KB
doc:beam/5b048fde-0e90-41b4-bd79-29398c7ac010Show excerpt
- **Solution**: Fine-tune indexing parameters and use approximate nearest neighbor (ANN) methods to find the right balance. ### Detailed Analysis and Solutions #### Scalability Issues **Potential Roadblock**: As the dataset grows, the…
ctx:claims/beam/9aef4a43-c110-4730-bed6-18e6312b77adctx:claims/beam/deee8e59-885e-45e2-98e2-b079298375cc- full textbeam-chunktext/plain1 KB
doc:beam/deee8e59-885e-45e2-98e2-b079298375ccShow excerpt
- `IndexIVFPQ` is used instead of `IndexIVFFlat` to provide faster approximate nearest neighbor search. 2. **Tuning Parameters**: - `nlist`: Number of clusters. A higher value can improve accuracy but also increases memory usage. …
ctx:claims/beam/8c21f541-c703-4998-aae0-19638ef54326- full textbeam-chunktext/plain1 KB
doc:beam/8c21f541-c703-4998-aae0-19638ef54326Show excerpt
faiss.omp_set_num_threads(8) # Adjust based on your CPU cores # Create a quantizer quantizer = faiss.IndexFlatL2(128) # Create an IVFPQ index nlist = 100 # Number of clusters M = 8 # Number of sub-quantizers nbits = 8 # Number of bits…
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