Run larger AI models on limited hardware.

QuantCore is an AI Memory Optimization Layer that compresses runtime LLM memory by up to 6x without accuracy loss, eliminating memory bottlenecks in real time.

Memory Reduction

0.995

Cosine Similarity (4-bit)

1 Line

Integration Code

How it works

HuggingFace Model
(Llama, Mistral, etc.)

→

                    QuantCore Layer

                    (Online Vector Quantization)

→

Compressed KV Cache
(Efficient Inference)

Memory Benchmark: Llama-3.1-8B KV Cache Size vs Context Length

Plug-and-play SDK

Works with existing models without retraining.

from transformers import AutoModelForCausalLM
from quantcore import optimize_model

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B"
)

# 1-line optimization
model = optimize_model(model, mode="balanced")

# Run inference normally
outputs = model.generate(input_ids)

Output Quality (Cosine Sim)

Cost Reduction Impact

Metric	Before (FP16)	After (QuantCore)	Impact
GPU Required (Batch 32, 4K ctx)	2x A100 (80GB)	1x A100 (80GB)	50% less hardware
Cost per hour (AWS)	$8.24	$4.12	Save $36,000 / year
Max Context (Single GPU)	~16k tokens	~64k tokens	4x longer memory