Quantized AI Deployment: We Cut One Client's Inference Costs from $12K to $2.8K/Month

Your AI model works. But it's costing you $10,000+/month in compute. Here's how to fix that.

What Is Model Quantization?

Model quantization reduces the precision of weights and activations in neural networks, significantly decreasing model size and inference costs while maintaining acceptable quality.

Simple explanation:

• Full precision (FP32): 32 bits per number (default training format)
• Half precision (FP16): 16 bits per number (2x smaller)
• 8-bit quantization (INT8): 8 bits per number (4x smaller)
• 4-bit quantization (INT4): 4 bits per number (8x smaller)

Result: Model size ÷ 2 (or ÷ 4 or ÷ 8) = Faster, cheaper inference

File Size Comparison Example (LLaMA-2-7B Model)

• Original Model (FP32): 28GB
• FP16: 14GB (50% reduction)
• INT8: 7GB (75% reduction)
• INT4: 3.5GB (87.5% reduction)

Real Cost Savings: The Math

Let's calculate actual cloud costs for serving 1 million requests per month using a LLaMA-2-7B model:

Configuration	GPU Type	Cost/Hour	Hours/Month	Monthly Cost
FP32 (unoptimized)	A100 (40GB)	$3.20	730	$2,336
FP16	A100 (40GB)	$3.20	365	$1,168
INT8 (recommended)	T4 (16GB)	$0.526	730	$384
INT4	T4 (16GB)	$0.526	365	$192

Savings from FP32 to INT8: $2,144/month (92% reduction)

Quality Loss: The Trade-Off You Need to Know

Quantization isn't free—there's a quality trade-off. Here's real performance data from our benchmark tests:

Test Scenario: LLaMA-2-7B on business document Q&A (our internal benchmark with 5,000 questions)

Configuration	Accuracy	F1 Score	Quality Loss
FP32 (baseline)	94.2%	0.938	0%
FP16	94.1%	0.936	0.1%
INT8 (sweet spot)	92.8%	0.921	1.5%
INT4	89.4%	0.887	5.1%

Sweet Spot for B2B: INT8 — Only 1.5% quality loss, but 83% cost savings. This is the optimal balance for most business applications.

When to Use Each Quantization Level

FP32 (No Quantization)

• Use for: Research & development, maximum accuracy required
• Don't use for: Production (too expensive)
• Cost: Highest (baseline)
• Quality: Highest

FP16

• Use for: Good balance between cost and quality
• Best for: Applications requiring near-perfect accuracy
• Cost: 2x speedup, 50% savings
• Quality: Minimal loss (0.1%)

INT8 (Recommended)

• Use for: Most B2B production applications
• Best for: Cost-conscious deployments where 1-3% accuracy loss is acceptable
• Cost: 4x speedup, 75% savings
• Quality: Small loss (1-3%)

INT4

• Use for: Maximum cost savings, non-critical applications
• Don't use for: Mission-critical systems
• Cost: 8x speedup, 90% savings
• Quality: Noticeable loss (5-8%)

Real Client Deployment: Before & After

Quantization Methods Compared

1. Post-Training Quantization (PTQ)

• When: Apply after model is fully trained
• Speed: Fast (30 minutes typical)
• Requirements: No retraining needed
• Quality loss: 2-4%
• Recommendation: Start here for most applications

2. Quantization-Aware Training (QAT)

• When: Train model with quantization in mind from the start
• Speed: Slow (requires full retraining)
• Requirements: Access to training data and pipeline
• Quality loss: 0.5-1% (minimal)
• Recommendation: Use only if PTQ quality isn't acceptable

Our Recommendation: Start with PTQ. Only move to QAT if quality isn't acceptable. In 85% of cases, PTQ is sufficient.

Our 4-Week Quantization Process

Week 1: Baseline & Benchmarking

• Establish current performance metrics
• Define acceptable quality thresholds
• Calculate current infrastructure costs

Week 2: PTQ Testing

• Test FP16, INT8, INT4 quantization
• Measure accuracy loss for each
• Benchmark latency and throughput

Week 3: Optimization

• Fine-tune quantization settings
• Test with production-representative data
• Load testing at scale

Week 4: Deployment

• Gradual rollout (A/B test)
• Monitor quality metrics closely
• Optimize further based on real-world performance

Quantization Tools We Use

1. PyTorch Quantization API

• Pros: Built into PyTorch, easy to use, well-documented
• Best for: PyTorch models

2. ONNX Runtime Quantization

• Pros: Cross-platform, excellent performance, Microsoft-backed
• Best for: Production deployments

3. TensorRT (NVIDIA)

• Pros: Maximum optimization for NVIDIA GPUs
• Cons: Complex setup, NVIDIA-only
• Best for: NVIDIA infrastructure

4. Hugging Face Optimum

• Pros: Easy quantization for transformer models, one-line implementation
• Best for: LLMs and transformer architectures

Common Quantization Mistakes (And How to Avoid Them)

Mistake 1: Quantizing Without Benchmarking

Solution: Always test on representative data first. Don't assume 2% accuracy loss—measure it.

Mistake 2: Going Straight to INT4

Solution: Start with INT8. Only go lower if acceptable. INT4's 5-8% quality loss is often too much.

Mistake 3: Not Testing Edge Cases

Solution: Test on hardest examples, not just average cases. Edge cases reveal quality problems.

Mistake 4: Quantizing the Wrong Layers

Solution: Some layers are more sensitive than others. Use mixed-precision quantization for critical layers.

Mistake 5: Ignoring Calibration Data

Solution: Use diverse calibration dataset (1,000-5,000 examples) representing real-world distribution.

Stratagem's Quantization Service

Express Package: $3,200

• PTQ quantization (FP16 + INT8)
• Performance benchmarking
• Production deployment guide
• 30 days support

Professional Package: $7,500

• PTQ + QAT if needed
• Multi-configuration testing
• Custom optimization
• Full deployment assistance
• 90 days support

Enterprise Package: Custom

• Ongoing quantization optimization
• Multi-model optimization
• Dedicated optimization team
• SLA guarantees

Average client savings: $6,800/month
Typical payback period: 18 days

Calculate Your AI Cost Savings

Want to know exactly how much you could save with quantization? We'll provide a free assessment including:

• Analysis of your current AI infrastructure costs
• Projected savings with INT8 quantization
• Expected quality impact for your specific use case
• ROI timeline and payback period
• Implementation roadmap

Request your free cost analysis or learn more about our AI optimization services.