CCQ: A New Frontier in Extreme Low‑bit Quantization for LLMs
Table of Contents
Introduction
The Quantization Challenge in LLMs
Why Go Below 4‑bit?
Existing Quantization Methods: Scalar & Vector
CCQ: Convolutional Code Quantization Explained
Key Innovations in CCQ
Bit‑shift encoding
Hardware‑aware optimization
Hybrid encoding
Code cluster
Lookup‑Free Encoding Space
Balancing Accuracy and Efficiency
Practical Workflow: Compressing DeepSeek‑V3
Compressing ERNIE‑4.5‑300B‑A47B to 89 GB
Benchmarks: Accuracy vs. Bit‑Width
Inference Performance Gains
Single‑GPU Deployment
Eliminating Inter‑Card Communication
Open‑Source Release for ERNIE 4.5‑300B
Implications for Adoption and Accessibility
Limitations and Fail‑Safe Considerations
Future Research Directions
Broader Impact on AI Infrastructure
Conclusion
1. Introduction
Large Language Models (LLMs) have driven major advances in natural-language understanding but at a steep inference cost. Traditional quantization methods reduce this cost but degrade performance below 3‑bit precision. CCQ (Convolutional Code Quantization) introduces a breakthrough 2.0–2.75‑bit quantization method, enabling high-accuracy LLM deployment at extreme low-bit settings 🚀.
2. The Quantization Challenge in LLMs
Quantization shrinks model size by mapping high-precision weights to fewer bits per parameter. Typical targets:
FP16 → 8‑bit: modest performance loss
4‑bit: still acceptable
Below 3‑bit: serious degradation due to quantization errors
Maintaining accuracy while going extremely low‑bit is difficult.
3. Why Go Below 4‑bit?
Moving from 4‑bit to 2‑bit offers dramatic advantages:
2× smaller model size
Half the memory bandwidth
Potential single‑GPU deployment for huge LLMs
Lower energy consumption
Yet performance must remain competitive.
4. Existing Quantization Methods: Scalar & Vector
Existing methods include:
Scalar Quantization (SQ): simple, speedy, but high error in low bits
Vector Quantization (VQ): groups data into learned codebooks—more accurate but slower and memory-heavy
Neither performs well below 3‑bit without notable accuracy loss.
5. CCQ: Convolutional Code Quantization Explained
CCQ merges traditional error-correcting Convolutional Code techniques with quantization. It includes:
Bit-shift encoding: hardware-aware mapping using optimized weight-bit transitions
Convolutional Code: error-resistant encoding of quantized weights
Hybrid encoding: combines convolutional and scalar techniques per weight cluster
Code clustering: shared codebooks across parameter clusters
Together, CCQ compresses models to 2.0–2.75 bits with minimal accuracy impact.
6. Key Innovations in CCQ
Bit‑shift encoding
Enables hardware‑accelerated transforms, avoiding slow arithmetic operations.
Hardware‑aware design
Designed for linear mapping—no lookups, enabling SIMD/Eigen speed.
Hybrid encoding
Mixes convolutional and standard encoding per cluster to balance complexity.
Code cluster
Groups similar weights to reduce codebook size and simplify encoding.
7. Lookup‑Free Encoding Space
Instead of storing large lookup tables, CCQ uses direct linear transforms. This removes:
Cache misses
Memory slowdown
Quantization round-off errors
It's faster, leaner, and fits into existing inference engines more easily.
8. Balancing Accuracy and Efficiency
CCQ preserves precision by:
Using trained encoding maps per cluster
Adapting to weight distributions
Employing error-aware correction via convolutional codes
Accuracy tests show performance close to standard 8‑bit models even at 2‑bit quantization.
9. Practical Workflow: Compressing DeepSeek‑V3
DeepSeek‑V3 (671B) is reduced to 184 GB — matching 2‑bit precision. Benefits:
Enables deployment on high‑end servers
Drops inter-GPU overhead
Cuts energy and memory usage
10. Compressing ERNIE‑4.5‑300B‑A47B to 89 GB
ERNIE‑4.5‑300B‑A47B goes from ~300GB to 89GB (2‑bit CCQ). This size fits in a single A100 GPU, eliminating the need for model parallelism and multi-card communication.
11. Benchmarks: Accuracy vs. Bit‑Width
CCQ across tasks (LLM benchmarks, QA, summarization) shows:
2.75‑bit models: within 0.5% of 8‑bit baseline
2.0‑bit models: within 1–2%—acceptable for many use cases
CCQ outperforms other 2‑bit solutions by 5–10%
12. Inference Performance Gains
CCQ’s hardware-aware transforms yield:
30–40% speedup vs. lookup‑based 4‑bit
2‑bit models surpass 4‑bit performance
Memory footprint halved, enabling better cache utilization
13. Single‑GPU Deployment
Large LLMs (>100B) normally need multi-GPU parallelism. CCQ enables:
ERNIE‑4.5‑300B on one A100
Lower latency, reduced communication bottlenecks
Broader deployment options (data center racks, edge)
14. Eliminating Inter‑Card Communication
Without sub-group pipelines, CCQ eliminates:
Collective operations
Synchronization delays
Memory transfer overhead
This unlocks better scalability across data center and edge setups.
15. Open‑Source Release for ERNIE 4.5‑300B
The 2‑bit ERNIE‑4.5 model and optimized inference engine are publicly released. This allows researchers to run advanced models on single GPUs, democratizing access.
16. Implications for Adoption and Accessibility
CCQ:
Makes large LLMs accessible to smaller organizations
Reduces inference cost and infrastructure barriers
Supports on-premise and privacy-aware deployment
17. Limitations and Fail‑Safe Considerations
Potential drawbacks:
Approximate quantization may misstep in edge numeric tasks
Encoding training adds pipeline complexity
Requires hardware support for bit‑shifts
May involve slight retraining or calibration steps
18. Future Research Directions
Opportunities include:
Extending CCQ to activations and gradients
Combining CCQ with structured sparsity for further shrinkage
Automating cluster and codebook creation
Benchmarking across multimodal and retrieval‑augmented LLMs
19. Broader Impact on AI Infrastructure
CCQ’s success may lead to:
Widespread low-bit deployment
Rethink of LLM design for edge AI
New hardware optimizations for convolutional quantization
20. Conclusion
CCQ is a milestone in LLM quantization—achieving ultra-low-bit performance with minimal accuracy loss and inference overhead. By compressing ERNIE‑4.5‑300B to 2 bits and deploying it on a single GPU, CCQ proves that extreme quantization is both practical and scalable, ushering in a new era of efficient AI democratization.
