TurboQuant: Are Compression and Performance Worth the Hype?

# Entry

TurboQuant is a novel algorithm suite and library recently launched by Google. Its goal is to apply advanced quantization and compression to enormous language models (LLM) and vector search engines – crucial components of search-assisted generation (RAG) systems – to dramatically improve their performance. TurboQuant has been shown to effectively reduce cache usage to just 3 bits, without requiring model retraining or loss of accuracy.

How does he do it and is it really worth doing it so loudly? The article aims to answer these questions through a description and a practical example of its application.

# TurboQuant in a nutshell

Although LLM and vector search engines employ multidimensional vectors to process information with impressive results, this effort requires huge amounts of memory, which can cause earnest bottlenecks in the so-called key-value (KV) cache – an easily accessible “digital cheat sheet” of frequently used real-time search information. Managing larger context lengths scales KV cache accesses linearly, which severely limits memory capacity and computation speed.

Vector quantization (VQ) techniques used in recent years lend a hand reduce the size of text vectors to disperse bottlenecks, but they often introduce additional “memory overhead” and require quantization constants to be computed with full precision on miniature blocks of data, which partially undermines the reason for compression.

TurboQuant is a set of next-generation algorithms for advanced compression with zero loss of accuracy. It optimally solves the memory overhead problem using a two-step process supported by two complementary techniques:

• Polar Quantity: This is the compression technique used in the first stage. Compresses high-quality data by mapping vector coordinates to a polar coordinate system. This simplifies data geometry and eliminates the need to store additional quantization constants – a major cause of memory overhead.
• QJL (quantized Johnson-Lindenstrauss): The second stage of the compression process. It focuses on removing any errors introduced in the previous step, acting as a mathematical checker that applies a miniature, one-bit compression to remove hidden errors or residual errors resulting from the employ of PolarQuant.

Is TurboQuant worth the hype?

According to experimental results and evidence, the tiny answer is: Yes. Avoiding the costly data normalization required in classic quantization approaches, 3-bit TurboQuant provides: 8x increase in performance over 32-bit unquantized keys on the H100 GPU-based accelerator.

# TurboQuant Rating

The following Python code example illustrates how developers can evaluate this locally. The program can be run in a local IDE or in the Google Colab notebook environment, providing a conceptual comparison between unquantized vectors and TurboQuant speedy compression.

TurboQuant repositories require specific kernels to run. For this example to work, first perform the following installations – preferably in a notebook environment unless you have plenty of disk space on your local computer.

First install TurboQuant:

In Google Colab, simply install the library and ensure that the runtime hardware accelerator is set to the T4 GPU – available in the free Colab tier – for the code below to work properly.

The following code illustrates a straightforward comparison of performance and memory usage when using a pre-trained language model with and without KV TurboQuant compression. First of all, we will need import:

import torch
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
from turboquant import TurboQuantCache

We will load a not very enormous LLM file TinyLlama/TinyLlama-1.1B-Chat-v1.0trained to generate text and an appropriate tokenizer. We specify to employ 16-bit decimal precision floating point: this option is usually more capable on contemporary hardware.

model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)

We then define the scenario by simulating the input string of a enormous model, because TurboQuant really shines when context windows get larger. Don’t worry about repeating the same content 20 times throughout the text: it’s the size that matters here, not the language itself.

prompt = "Explain the history of the universe in great detail. " * 20 
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

The following function is the key to measuring and comparing execution time and memory consumption throughout the text generation process, using TurboQuant 3-bit quantization: use_tq=True or deactivated, use_tq=False. The cache is flushed first to ensure tidy measurements.

def run_unified_benchmark(use_tq=False):
    torch.cuda.empty_cache()
    
    # Initializing the specific cache type
    cache = TurboQuantCache(bits=3) if use_tq else None
    
    start_time = time.time()
    with torch.no_grad():
        # Running the model to generate output tokens
        outputs = model.generate(**inputs, max_new_tokens=100, past_key_values=cache)
    
    duration = time.time() - start_time
    
    # Isolating the Cache Memory
    # Instead of measuring the whole 2GB model, we measure the generated Cache size
    # For a 1.1B model: [Layers: 22, Heads: 32, Head_Dim: 64]
    num_tokens = outputs.shape[1]
    elements = 22 * 32 * 64 * num_tokens * 2 # Key + Value
    
    if use_tq:
        mem_mb = (elements * 3) / (8 * 1024 * 1024) # 3-bit calculation
    else:
        mem_mb = (elements * 16) / (8 * 1024 * 1024) # 16-bit calculation
        
    return duration, mem_mb

Finally, we perform the process twice – once with each of the two specified settings – and compare the results:

base_time, base_mem = run_unified_benchmark(use_tq=False)
tq_time, tq_mem = run_unified_benchmark(use_tq=True)

print(f"--- THE VERDICT ---")
print(f"Baseline (FP16) Cache: {base_mem:.2f} MB")
print(f"TurboQuant (3-bit) Cache: {tq_mem:.2f} MB")
print(f"Speedup: {base_time / tq_time:.2f}x")
print(f"Memory Saved: {base_mem - tq_mem:.2f} MB")

Results:

--- THE VERDICT ---
Baseline (FP16) Cache: 42.45 MB
TurboQuant (3-bit) Cache: 7.86 MB
Speedup: 0.61x
Memory Saved: 34.59 MB

The compression ratio is impressively up to 5.4x with respect to the KV cache footprint. But what about acceleration? Is it as expected with TurboQuant? Not entirely, but this is normal because the sequence we used is still considered tiny for the large-scale scenarios that TurboQuant is intended for, and we run it on local infrastructure, not at scale. The real speed gain with TurboQuant comes as you simultaneously scale the context length and hardware accelerators used. Consider an enterprise-level H100 GPU cluster and long RAG messages of over 32,000. tokens: in such scenarios, memory traffic is significantly reduced, and with TurboQuant you can expect up to 8x raise in throughput.

In summary, there is a trade-off between memory bandwidth and computational latency, which can be further confirmed by trying different settings for the input and output sizes, e.g. multiplying the input string by 200 and setting max_new_tokens=250you might get something like this:

--- THE VERDICT ---
Baseline (FP16) Cache: 421.44 MB
TurboQuant (3-bit) Cache: 79.02 MB
Speedup: 0.57x
Memory Saved: 342.42 MB

Ultimately, TurboQuant’s transformative performance for AI models is demonstrated by its ability to maintain high precision while operating at 3-bit system performance in large-scale environments.

# Summary

This article introduces TurboQuant and discusses whether it is worth the hype in terms of compression and performance compared to other classic quantization methods used in LLM and other large-scale inference models.

Ivan Palomares Carrascosa is a thought leader, writer, speaker and advisor in the fields of Artificial Intelligence, Machine Learning, Deep Learning and LLM. Trains and advises others on the employ of artificial intelligence in the real world.