# TurboQuant Pro: 27x Memory Compression for AI Vector Databases — Now with Autotune, FAISS, and vLLM

*Andrew H. Bond | San Jose State University*

---

Every AI system that uses embeddings faces the same problem: vectors are expensive to store.

A single 1024-dimensional embedding in float32 takes 4,096 bytes. That sounds small until you scale. A RAG system with 2.4 million document chunks requires 9.4 GB just for the embeddings. Add HNSW indexes and you're looking at 15-20 GB of RAM for a single pgvector table. On a system with multiple embedding stores (semantic, episodic, ethics), you quickly exhaust available memory.

## The Core Technique: PCA-Matryoshka

Most deployed embedding models (BGE-M3, Cohere Embed, older OpenAI models) weren't trained with Matryoshka representation learning, so naive dimension truncation destroys them — cosine similarity drops to 0.467 at half dimensions.

PCA-Matryoshka fixes this with a training-free rotation: fit PCA once on a sample, then rotate all vectors so truncation works. Combined with 3-bit scalar quantization (TurboQuant: rotation + Lloyd-Max centroids), the full pipeline delivers:

| Method | Compression | Cosine Sim | Recall@10 |
|--------|------------|-----------|-----------|
| Scalar int8 | 4x | 0.9999 | 97.2% |
| TurboQuant 3-bit | 10.6x | 0.978 | 83.8% |
| **PCA-384 + TQ3** | **27.7x** | **0.979** | **76.4%** |
| PCA-128 + TQ2 | 113.8x | 0.924 | 78.7% |
| Binary quantization | 32x | 0.758 | 66.6% |
| Product Quantization | 256x | 0.810 | 41.4% |

PCA-Matryoshka + TQ fills the gap in the Pareto frontier between scalar quantization (<10x) and binary/PQ (>32x), strictly dominating both binary and product quantization across the practical range.

## Real Numbers

We applied this to our production embedding workloads:

| Dataset | Vectors | Float32 | Compressed | Ratio |
|---------|---------|---------|------------|-------|
| Ethics corpus | 2.4M | 9,375 MB | 338 MB | 27x |
| Publications | 824K | 3,222 MB | 116 MB | 27x |
| RAG chunks | 112K | 437 MB | 16 MB | 27x |
| **Total** | **3.3M** | **13 GB** | **470 MB** | **27x** |

Compression runs at 100,000 embeddings per second on CPU, with optional CuPy GPU acceleration for 2.1M/sec.

## What's New in v0.5

### 1. Autotune CLI

The number-one question after release: "Which configuration should I use?" Autotune answers this in 10 seconds:

```bash
turboquant-pro autotune --source "dbname=mydb user=me" --min-recall 0.95
```

It connects to your PostgreSQL database, samples embeddings, sweeps 12 configurations, and recommends the highest compression meeting your recall threshold. On our 194K corpus, it found that PCA-384 + TQ4 gives 20.9x compression at 96.0% recall — saving 722 MB.

### 2. FAISS Integration

Wraps FAISS indices with automatic PCA compression. Your 1024-dim embeddings get PCA-rotated and truncated before indexing. Queries are auto-rotated at search time.

```python
from turboquant_pro.faiss_index import TurboQuantFAISS

index = TurboQuantFAISS(pca, index_type="ivf", n_lists=100)
index.add(corpus)  # Compressed automatically
distances, ids = index.search(query, k=10)
```

Supports Flat, IVF, and HNSW. Same FAISS API, 2.7x smaller index.

### 3. vLLM KV Cache Plugin

The KV cache in transformer inference has the same compression opportunity. TurboQuant's 3-bit quantization with hot/cold tiering:

- **Hot window** (last 512 tokens): Full precision, zero latency
- **Cold storage** (older tokens): 3-bit compressed, ~5x smaller

For Gemma 4 31B at 8K context: KV cache drops from ~2 GB to ~340 MB. Or keep the same memory and run 4x longer context.

## The Full Stack

| Use Case | Module | Compression |
|----------|--------|-------------|
| pgvector RAG | `TurboQuantPGVector` | 15-114x |
| FAISS search | `TurboQuantFAISS` | 2-8x (dims) |
| LLM KV cache | `TurboQuantKVManager` | ~5x |
| NATS transport | `TurboQuantNATSCodec` | 10.5x |
| Find optimal config | `turboquant-pro autotune` | Auto |

175 tests passing. MIT licensed. Core dependency: just NumPy.

## NEW: Native PostgreSQL Extension (Rust)

We also shipped `tqvector` — a native PostgreSQL extension written in Rust (pgrx) with optional CUDA acceleration:

```sql
-- Compress your entire embedding table
CREATE TABLE embeddings_tq AS
SELECT id, tq_compress(embedding::float4[], 3) AS tqv
FROM embeddings;

-- Search with cosine distance
SELECT id, tqv <=> query_tqv AS dist
FROM embeddings_tq ORDER BY dist LIMIT 10;
```

Benchmarked on 194K production vectors: **23,969 vec/sec compression**, **5.2 GB → 169 MB** (31x). No Python in the loop — pure Rust inside PostgreSQL.

## What's Next

- Compressed HNSW index (search without decompression)
- Async vLLM backend for non-blocking KV offload
- ADC search (approximate distance in compressed space)

---

**PyPI:** `pip install turboquant-pro[all]` (v0.5.0)
**GitHub:** https://github.com/ahb-sjsu/turboquant-pro
**Paper:** IEEE TAI submission — 15-method comparison on 2.4M vectors across 37 languages

*Andrew H. Bond is a consultant and researcher working on distributed AI architectures and embedding compression at San Jose State University.*

#MachineLearning #VectorDatabases #Compression #OpenSource #pgvector #FAISS #vLLM #Embeddings
