⚡ QuantForge

AI-Driven Compression Engine for LLMs & Vector Search

Near-optimal vector quantization within 2.7× of Shannon's limit — no calibration, no training, works on any data.

Why This Matters • Overview • Key Results • Quick Start • Benchmarks • Features

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🚨 Why This Matters

Large Language Models are bottlenecked by memory bandwidth, not compute.

• KV cache dominates inference cost
• Vector databases struggle at billion-scale
• Quantization typically requires calibration or retraining

QuantForge solves this by:

• Compressing KV cache by 4–8× with no retraining
• Fusing quantization directly into attention kernels
• Maintaining high accuracy (>85% Recall@10 at 4-bit)

→ Result: Significantly lower inference cost and higher throughput

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🖼️ System Overview

graph TD
    classDef core fill:#1e40af,stroke:#60a5fa,stroke-width:2px,color:white;
    classDef storage fill:#065f46,stroke:#34d399,stroke-width:2px,color:white;
    classDef compute fill:#4c1d95,stroke:#a78bfa,stroke-width:2px,color:white;

    Input[Input Vectors] --> Engine

    subgraph QuantForge[QuantForge Compression Engine]
        Engine[QuantPipeline] --> Transform[Hadamard Transform]
        Transform --> Quantizer[Lloyd-Max Quantizer]
        Quantizer --> Tensor[QuantizedTensor<br/>codes + scale + metadata]
    end

    Tensor --> VectorDB[Vector Search<br/>IVF Index]
    Tensor --> LLM[LLM KV Cache<br/>Block KV Storage]
    Tensor --> API[REST API / CLI]

    VectorDB --> GPU
    LLM --> GPU

    subgraph Acceleration [Hardware Execution]
        GPU[Fused Quantized Attention<br/>Triton / CUDA]
        Opt[Bayesian Optimizer<br/>Accuracy vs Latency]
    end
    
    GPU --> Opt

    class Engine,Transform,Quantizer core;
    class Tensor storage;
    class GPU,Opt compute;

Loading

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📊 Key Results

• 🔹 4–8× KV cache compression (no retraining)
• 🔹 >85% Recall@10 at 4-bit (1M vectors)
• 🔹 ~1e-3 numerical deviation vs FP16 attention
• 🔹 5× memory reduction on Llama-2 7B
• 🔹 Linear scaling across GPUs (TP simulation)

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🚀 Quick Start

pip install .

Compress Vectors (3 lines)

from quantforge import QuantPipeline, QuantForgeConfig

pipeline = QuantPipeline(dim=768, config=QuantForgeConfig(bits=4))
qt = pipeline.compress(embeddings)        # → QuantizedTensor (4× smaller)
reconstructed = pipeline.decompress(qt)   # → np.ndarray (original shape)

Vector Search (FAISS-like)

from quantforge.vectordb import QuantizedIndex

index = QuantizedIndex(dim=768, config=QuantForgeConfig(bits=4))
index.add(database_vectors)               # Quantize + index
ids, scores = index.search(query, k=10)   # ANN search

LLM KV Cache Compression

from transformers import AutoModelForCausalLM
from quantforge.llm import patch_model

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
model = patch_model(model)  # KV cache now uses 4-bit quantization
outputs = model.generate(**inputs)

🎬 Demo

quantforge benchmark data.npy --bits 4
quantforge compress embeddings.npy --bits 4 --output compressed.npz
quantforge optimize data.npy
quantforge serve --port 8000

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📈 Benchmarks

Real-World Scaling Limits (Sentence Transformers)

We benchmarked QuantForge up to 1M scale using embeddings from sentence-transformers/all-MiniLM-L6-v2.

The 4-bit configuration preserves >85% Recall at 1M clusters while maintaining ~10ms execution times per query on typical infrastructure.

Quantization Quality (MSE vs Theory)

Bits	QuantForge MSE	Paper Reference	Upper Bound (Thm 1)	Compression
1	0.360	0.36	0.384	16×
2	0.117	0.117	0.096	8×
3	0.030	0.03	0.024	5.3×
4	0.009	0.009	0.006	4×

Vector Search (Recall@10)

Method	Recall@10	Memory	Calibration
Exact (FP64)	1.000	100%	—
QuantForge 4-bit	~0.95	25%	None
QuantForge 2-bit	~0.75	12.5%	None
Naive Uniform 4-bit	~0.85	25%	Required

KV Cache Compression

Model	FP16 Memory	QuantForge 4-bit	QuantForge 3-bit	Speedup
Llama-2 7B	2.0 GB	~500 MB	~375 MB	4–5×
Mistral 7B	1.8 GB	~450 MB	~340 MB	4–5×

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⚡ Fused Quantized Attention

QuantForge implements a fully fused attention kernel:

• Dequantization happens inside SRAM
• Softmax computed via log-sum-exp (numerically stable)
• No intermediate tensor materialization

This removes memory bandwidth bottlenecks and enables efficient inference at low bit-widths.

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🆚 Comparison

System	Compression	Training Required	Fused Attention	GPU Optimized
FAISS PQ	✔	✔	❌	Partial
vLLM	❌	❌	✔	✔
QuantForge	✔	❌	✔	✔

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🎯 Use Cases

• LLM inference optimization: KV cache compression without training loops.
• Vector search at scale: ANN algorithms with highly reduced memory bounds.
• Edge deployment: Low-memory environments processing intelligence loops.
• Research: Systems engineering in quantization scaling constraints and ML bounds.

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✨ Features

Core Engine

• Lloyd-Max optimal quantization — iterative centroid optimization for Gaussian distribution
• Fast Walsh-Hadamard Transform — O(d log d) rotation replacing O(d³) QR decomposition
• Scale management — QuantizedTensor tracks scale, zero-point, and transform state for lossless reconstruction
• Dtype preservation — explicit float32/float16 handling for HuggingFace/vLLM interop

Vector Database

• IVF partitioning — K-means++ initialized Inverted File Index for sub-linear search
• Multi-probe search — configurable n_probe for recall/speed trade-off
• Brute-force fallback — automatic for datasets < 10K vectors
• Memory reporting — detailed per-partition memory accounting

LLM Integration

• HuggingFace patch — non-invasive register_forward_hook (supports Llama, Mistral, Phi, Gemma, Qwen2)
• vLLM PagedAttention — concrete hook points for CacheEngine, FlashAttentionBackend, BlockSpaceManager
• Per-head quantization — each attention head gets an independent quantizer
• Block-structured cache — append-only paged storage matching vLLM's architecture

GPU Acceleration

• Triton JIT kernels — vectorized nearest-centroid quantization on GPU
• Automatic fallback — seamless NumPy backend when Triton/CUDA unavailable
• Zero behavior difference — identical results regardless of backend

AI Optimizer

• Multi-objective reward — balances accuracy, compression, and latency
• Search policy — random or exhaustive exploration of bit-width × transform × normalization
• Human-readable recommendations — optimizer.recommend(data) prints actionable advice

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🏗️ Architecture

Input Vectors ──→ QuantPipeline ──→ QuantizedTensor (codes + scale + metadata)
                       │
        ┌──────────────┼──────────────┐
        │              │              │
   Transform      Quantizer      Storage
  (Hadamard/QR)  (Lloyd-Max)   (QuantizedTensor)
        │              │              │
        └──────────────┼──────────────┘
                       │
     ┌─────────────────┼─────────────────┐
     │                 │                 │
  VectorDB        LLM KV Cache      API/CLI
 (IVF Index)    (Block KV Layout)   (FastAPI)
     │                 │                 │
     └────────┬────────┘                 │
              │                          │
        Triton Kernels              Benchmarks
       (with NumPy fallback)
              │
        AI Optimizer
     (Policy + Reward)

Package Layout

quantforge/
├── core/                  # TurboQuant++ engine
├── fastops/               # Optimized transforms
├── vectordb/              # FAISS-like search
├── llm/                   # LLM integration
├── triton/                # GPU acceleration
├── optimizer/             # AI brain
├── api/                   # REST API
├── utils/                 # Infrastructure
└── cli.py                 # CLI entry point

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📖 API Reference

Core

Class	Description
QuantPipeline(dim, config)	End-to-end compress/decompress pipeline
TurboQuantizer(dim, config)	Low-level quantizer with encode/decode
QuantizedTensor	Immutable container for quantized data + metadata
QuantForgeConfig	Centralized configuration with auto-detection

Vector DB

Method	Description
index.train(vectors)	Train IVF centroids (K-means++)
index.add(vectors)	Add vectors to index
index.search(query, k)	ANN search, returns (ids, scores)

LLM

Function	Description
patch_model(model, config)	Patch HF model for quantized KV cache
unpatch_model(model)	Remove QuantForge hooks

REST API

Endpoint	Method	Description
/health	GET	Health check
/compress	POST	Quantize vectors
/decompress	POST	Reconstruct vectors
/benchmark	POST	Run compression benchmark
/optimize	POST	Find optimal configuration

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⚖️ Design Tradeoffs & Limits

Building realistic ML infrastructure requires understanding architectural boundaries.

• Hadamard vs. QR Transform:
  • Hadamard operates in $O(d \log d)$ and requires negligible memory allocation. It is our primary configuration unless vector dimensions cannot be cleanly padded.
  • QR Random Matrix is strictly $O(d^3)$ to generate and requires $d^2$ memory to host. We fall back to this only when explicitly forced.
• Triton vs Native Host:
  • The fused_quant_dot kernel pushes decompression to SRAM. Without Triton (e.g. on standard Mac/Windows CPUs), the exact same bitwise tensor requires PyTorch memory materialization, creating temporary memory bandwidth bottlenecks.

⚠️ Limitations

• Causal fused attention not yet implemented
• Extreme outliers may affect Lloyd-Max optimality
• Multi-node distributed execution requires external orchestration

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🔭 Future Scope

While QuantForge is production-ready for bidirectional and embedding-centric paradigms, the following architectures are under active exploration for future deployment:

1. Causal Fused Attention (Triton)

Our current implementation focuses on bidirectional (unmasked) fused_quant_attention to ensure absolute stability and minimal divergent branching within the kernel. Next Steps: Introduce structural masking within the SRAM calculation loop to inherently support decoder-only causal processing dynamically, completely matching raw FlashAttention causality constraints block-by-block.

2. Learned Importance Predictors

Tokens naturally assert asymmetric context gravity (i.e. specific nouns act as primary anchors, while connector strings can afford heavy information loss). Next Steps: Rather than deploying simple $L_2$ norm heuristics for optimization targets, we plan to train a lightweight predictor network routing "low-impact" tokens identically to extreme 2-bit aggressive strategies while routing "attentional-sink" tokens dynamically toward 8-bit configurations entirely auto-regressively.

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🔬 Theoretical Foundation

From TurboQuant (Zandieh et al., 2025):

Step 1 — Random Rotation: Multiply by a Haar-distributed orthogonal matrix. After rotation, each coordinate follows a Beta distribution (≈ Gaussian in high dimensions), regardless of input.

Step 2 — Lloyd-Max Quantization: Since coordinates are now approximately i.i.d., apply the optimal 1D scalar quantizer. The codebook is precomputed and cached.

Step 3 — QJL Correction (inner-product variant): Apply a 1-bit Quantized Johnson-Lindenstrauss transform to the residual to correct bias in inner-product estimation.

Guarantees:

• MSE: E[‖x − x̂‖²] ≤ (√3π/2) · 4^{−b} (Theorem 1)
• Inner product: Unbiased with variance ≤ (√3π/2) · ‖y‖²/d · 4^{−b} (Theorem 2)
• Lower bound: No quantizer can achieve MSE below 4^{−b} (Theorem 3)

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🧪 Running Tests

# Install dev dependencies
pip install -e ".[dev]"

# Run all tests
pytest tests/ -v

# Run specific test
pytest tests/test_core.py -v

# Run benchmarks
python -m benchmarks.full_benchmark

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📄 Citation

@article{zandieh2025turboquant,
  title   = {TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate},
  author  = {Zandieh, Amir and Daliri, Majid and Hadian, Majid and Mirrokni, Vahab},
  journal = {arXiv preprint arXiv:2504.19874},
  year    = {2025}
}

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📜 License

MIT License — see LICENSE for details.