Quantization is rapidly evolving from a research concept to a practical necessity in enterprise AI. By reducing memory usage by 2× to 4× and accelerating inference with minimal impact on accuracy, production-ready quantization makes large language models (LLMs) viable with super computer environments. This blog explores why quantization is crucial, how we’ve industrialized the process, and what real-world results we’ve achieved.

Why quantization?

The GPU Memory Wall

As GPU compute performance scales, memory bandwidth hasn’t kept pace—creating a “memory wall” that limits the deployment of large models. For example, Meta’s Llama-3.1-405B and Llama-4-Maverick (each ~400B parameters) require ~750GB of memory in FP16, demanding at least two NVIDIA H100 nodes. That kind of infrastructure comes at a steep cost, restricting accessibility.

And it’s not just the weights—activation memory (KV cache, attention maps) also adds up fast. Quantization helps shrink this footprint significantly.

What Quantization delivers

Quantization trades a small degree of precision for significant gains in efficiency:

  • Memory: FP8 weights reduce memory use by half compared to FP16.
  • Speed: FP8 operations run 2× faster on NVIDIA H100s.
  • Cost & Energy: Same throughput with 50% fewer GPUs.
  • Accuracy: Dynamic FP8 consistently maintains 99–100% model quality compared to the original FP16.

Dynamic quantization techniques adjust scale factors per tensor, token, or channel to maintain high fidelity.

Our Quantization Framework

We’ve built a robust quantization pipeline designed to integrate open-source and in-house solutions, automate evaluation, and support diverse LLM architectures.

Key Features:

  • Flexible Quantization API: Unified interface for multiple model types and techniques.
  • Configurable Algorithms: Supports dynamic FP8, INT4, and other precision levels.
  • Deployment Optimization: Produces quantized weights ready for efficient serving.

Benchmarking at Scale

To help ensure quantized models meet production standards, we developed a fully automated benchmarking framework:

  • Serving Performance Metrics at both Request and Server Levels:
    • Time To First Token (TTFT)
    • Time Per Output Token (TPOT)
    • Server Throughput
  • Model Quality Check: Measures the recovery rate of model accuracy vs. the original model
  • Pareto Analysis: Helps choose the best trade-off between serving performance and model accuracy
  • Workload Coverage: Includes short/long prefill and decode scenarios using real-world and domain-specific datasets

Addressing Throughput Bottlenecks

Our quantization techniques have delivered tangible improvements across several high-profile LLM deployments. For the multimodal Llama 3.2-90B model, applying FP8 quantization led to a 10% reduction in inference latency while maintaining nearly identical throughput—all while using only half the number of GPUs. With Llama 3.3-70B, we achieved 99%+ model quality recovery, along with a 30% reduction in latency and a 50% boost in server throughput when using same number of GPUs. Additionally, we have been partnering with Lab scientists to experiment innovative INT4 quantization featuring custom kernels and fine-grained layer tuning to optimize server throughput, which is missing from many open-source INT4 algorithms. Initial results show over 50% improvement in per-GPU throughput and reduced GPU requirements to just 25% of the original FP16, all with competitive model accuracy.

Guidance for Practitioners

Based on our implementation experience, successful production deployment of quantized models hinges on three key practices. First, automate benchmarking to help ensure measurable, reproducible performance data guides every optimization—this supports accelerated development and informed decision-making. Second, aim for balanced performance tuning: optimizing solely for request-level latency often degrades server throughput, so it’s essential to consider both metrics to achieve reliable, scalable serving. Finally, treat quantization as a production requirement rather than a research experiment—it is a foundational technique for making large language models deployable, affordable, and environmentally sustainable in real-world systems.

What’s next

Looking ahead, our focus is on pushing the boundaries of low-bit quantization and hardware-aware optimization. We are actively exploring in several aspects: 4-bit quantization with competitive performance in both latency and throughput; sub-4-bit quantization methods, including 2-bit and 3-bit precision, with the goal of preserving model quality while further reducing memory and compute demands; hardware-specific quantization strategies that align with the architectural strengths of emerging accelerator platforms. By co-designing quantization techniques with hardware capabilities, we aim to unlock even greater efficiencies and performance gains for enterprise-scale AI deployment.

Quantization is not just an optimization—it’s the bridge between AI innovation and AI at scale. With production-grade tools and real-world validation, we’re unlocking enterprise-ready performance for today’s largest models.

For more information, reach out to Oracle sales representative, or try out our Generative AI Service to discover the power of generative AI models equipped with advanced language comprehension for building the next generation of enterprise applications.