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Ivan Pleshkov
@@ -18,8 +18,6 @@ category: qdrant-internals
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weight: -200
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---
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# TurboQuant in Qdrant
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If you run production vector workloads, you already know the compression ladder in Qdrant: float32 is the baseline, **Scalar Quantization (SQ)** compresses vectors by 4× with almost no recall hit, and **Binary Quantization (BQ)** packs vectors at 16× or 32×.
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Qdrant 1.18 ships **TurboQuant** — a new rotation-based vector quantization method from [Google Research](https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/), with extensions that make it work on real production embeddings. Summarizing the results of benchmarks across public embedding datasets:
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@@ -66,23 +64,19 @@ The `bits` field controls encoding bit depth. It defaults to `bits4`. Available
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## At a Glance
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Recall, HNSW (`m=16`, `ef_construct=128`). Four datasets shown here for orientation; the full dataset table is [further down](#detailed-benchmarks).
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Recall, HNSW (`m=16`, `ef_construct=128`), on four representative datasets — [arxiv-instructorxl-768](https://huggingface.co/datasets/Qdrant/arxiv-titles-instructorxl-embeddings), dbpedia-gemini [TODO link], dbpedia-openai-ada [TODO link], and [wiki-cohere-v3-1024](https://huggingface.co/datasets/Cohere/wikipedia-2023-11-embed-multilingual-v3). The full ten-dataset table is [further down](#detailed-benchmarks).
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| Dataset | f32 | SQ | **TQ 4-bit** | TQ 2-bit | BQ 2-bit | TQ 1-bit | BQ 1-bit |
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| ----------------------------------------------------------------------------------------------------- | ------ | ------ | ------------ | ---------- | -------- | ---------- | -------- |
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| [arxiv-instructorxl-768](https://huggingface.co/datasets/Qdrant/arxiv-titles-instructorxl-embeddings) | 0.9419 | 0.9285 | **0.9193** | **0.8227** | 0.6756 | **0.6763** | 0.4683 |
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| dbpedia-gemini [TODO link] | 0.9167 | 0.9134 | **0.9020** | **0.8170** | 0.6689 | **0.6990** | 0.4945 |
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| dbpedia-openai-ada [TODO link] | 0.9625 | 0.8839 | **0.9299** | **0.8480** | 0.7332 | **0.7356** | 0.6098 |
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| [wiki-cohere-v3-1024](https://huggingface.co/datasets/Cohere/wikipedia-2023-11-embed-multilingual-v3) | 0.9446 | 0.9014 | **0.9271** | **0.8303** | 0.6880 | **0.6300** | 0.5409 |
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| **Compression** | 1× | 4× | **8×** | 16× | 16× | 32× | 32× |
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**1. TQ 4-bit is competitive with SQ at half the storage.** On `arxiv-instructorxl` and `dbpedia-gemini` it is about 1 pp below SQ; on `dbpedia-openai-ada` and `wiki-cohere-v3` it actually *beats* SQ by up to 4.6 pp.
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`BQ 1-bit` is the vanilla 1-bit configuration (1-bit storage, 1-bit query). The asymmetric variant (8-bit query) is included separately in the [detailed table](#detailed-benchmarks).
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{{< figure src="/articles_data/turboquant/at-a-glance-4bit.svg" alt="Recall comparison: float32 baseline vs SQ (4×) vs TurboQuant 4-bit (8×) across four datasets" caption="float32 baseline, SQ (4× compression), and TurboQuant 4-bit (8× compression)." width="100%" >}}
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Three main observations:
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**2. TQ 2-bit beats BQ 2-bit by 11–15 pp** on these four datasets (and 9–24 pp across all ten datasets), at the same 16× storage.
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1. **TQ 4-bit is competitive with SQ at half the storage.** On `arxiv-instructorxl` and `dbpedia-gemini` it is about 1 pp below SQ; on `dbpedia-openai-ada` and `wiki-cohere-v3` it actually *beats* SQ by up to 4.6 pp.
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2. **TQ 2-bit beats BQ 2-bit by 11–15 pp** on these four datasets (and 9–24 pp across all ten datasets), at the same 16× storage.
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3. **TQ 1-bit beats vanilla BQ 1-bit by 9–21 pp** on these four datasets (and 9–21 pp across all ten datasets), at the same 32× storage.
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{{< figure src="/articles_data/turboquant/at-a-glance-2bit.svg" alt="Recall comparison at 16× compression: TurboQuant 2-bit vs Binary Quantization 2-bit" caption="At 16× compression — TurboQuant 2-bit vs Binary Quantization 2-bit." width="100%" >}}
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**3. TQ 1-bit beats vanilla BQ 1-bit by 9–21 pp** on these four datasets (and 9–21 pp across all ten datasets), at the same 32× storage. `BQ 1-bit` here is the vanilla 1-bit configuration (1-bit storage, 1-bit query); the asymmetric variant (8-bit query) is in the [detailed table](#detailed-benchmarks).
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{{< figure src="/articles_data/turboquant/at-a-glance-1bit.svg" alt="Recall comparison at 32× compression: TurboQuant 1-bit vs vanilla Binary Quantization 1-bit" caption="At 32× compression — TurboQuant 1-bit vs vanilla Binary Quantization 1-bit." width="100%" >}}
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## What Is TurboQuant?
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@@ -116,8 +110,7 @@ Vanilla MSE has a persistent length bias: quantized vectors are systematically s
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The fix we use here comes from **[RaBitQ](https://arxiv.org/abs/2405.12497)** rather than from the TurboQuant paper itself: store one extra per-vector scalar that records how much the quantization shrank the length, and multiply it back in at scoring time. We pay the same 4 bytes per vector that we already reserve for the L2 length and use them to store the **ratio of original length to centroid-reconstruction length**.
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TODO: diagram
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{{< figure src="/articles_data/turboquant/length-renormalization.svg" alt="2D illustration of length renormalization: the quantized vector is short and slightly rotated; multiplying by the stored ratio scales it back to the original length and lands it much closer to the original vector" caption="The quantized vector is shorter than the original and points in a slightly different direction. Multiplying by the stored ratio scales it back to the original length — the renormalized vector lands on the same circle as the original, much closer to it than the raw quantized one." width="100%" >}}
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TurboQuant's PROD variant spends an entire QJL random projection plus extra bits in the codebook on the same problem; RaBitQ-style renormalization spends 4 bytes and one multiplication.
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<text x="651.0" y="92.8" text-anchor="middle" font-size="11" fill="#0F172A" font-weight="500">0.901</text>
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<text x="697.7" y="85.8" text-anchor="middle" font-size="11" fill="#0F172A" font-weight="500">0.927</text>
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<text x="651.0" y="360.0" text-anchor="middle" font-size="12" fill="#334155">wiki-cohere-v3-1024</text>
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<rect x="231.5" y="380.0" width="12" height="12" fill="#1E293B" rx="2"/>
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<text x="251.5" y="390.0" fill="#0F172A">float32</text>
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<rect x="324.5" y="380.0" width="12" height="12" fill="#4F46E5" rx="2"/>
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<text x="344.5" y="390.0" fill="#0F172A">SQ — 4×</text>
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<rect x="417.5" y="380.0" width="12" height="12" fill="#DC244C" rx="2"/>
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<text x="437.5" y="390.0" fill="#0F172A">TQ 4-bit — 8×</text>
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After Width: | Height: | Size: 4.3 KiB |
@@ -0,0 +1,27 @@
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<defs>
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<marker id="arrow" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="7" markerHeight="7" orient="auto-start-reverse">
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<path d="M 0 0 L 10 5 L 0 10 z" fill="context-stroke"/>
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</marker>
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<marker id="arrow-axis" viewBox="0 0 10 10" refX="9" refY="5" markerWidth="6" markerHeight="6" orient="auto-start-reverse">
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<path d="M 0 0 L 10 5 L 0 10 z" fill="#0F172A"/>
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<rect width="100%" height="100%" fill="#ffffff"/>
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<text x="380.0" y="30" text-anchor="middle" font-size="16" font-weight="600" fill="#0F172A">Length renormalization in 2D</text>
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<line x1="110" y1="380" x2="110.0" y2="20.0" stroke="#0F172A" stroke-width="1.2" marker-end="url(#arrow-axis)"/>
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<path d="M 425.0 380.0 A 315.0 315.0 0 0 0 110.0 65.0" fill="none" stroke="#0F172A" stroke-width="1.4" stroke-dasharray="4 4"/>
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<line x1="110" y1="380" x2="374.2" y2="208.4" stroke="#DC244C" stroke-width="3" marker-end="url(#arrow)"/>
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<line x1="110" y1="380" x2="215.7" y2="311.4" stroke="#64748B" stroke-width="2.6" stroke-dasharray="5 4" marker-end="url(#arrow)"/>
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<line x1="110" y1="380" x2="303.9" y2="131.8" stroke="#1E293B" stroke-width="3" marker-end="url(#arrow)"/>
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<circle cx="110" cy="380" r="3" fill="#0F172A"/>
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<circle cx="303.9" cy="131.8" r="4" fill="#1E293B"/>
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<circle cx="374.2" cy="208.4" r="4" fill="#DC244C"/>
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<circle cx="215.7" cy="311.4" r="4" fill="#64748B"/>
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<text x="315.9" y="123.8" text-anchor="start" font-size="16" font-weight="600" fill="#1E293B">x</text>
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<text x="388.2" y="212.4" text-anchor="start" font-size="16" font-weight="600" fill="#DC244C">ratio · x quantized</text>
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<text x="229.7" y="329.4" text-anchor="start" font-size="16" font-weight="600" fill="#64748B">x quantized</text>
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</svg>
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|
After Width: | Height: | Size: 2.2 KiB |
Reference in New Issue
Block a user