EDGE_DEPLOYMENT

Compress
LLMs by
75%

Deploy models on edge devices, reduce inference costs by 80%, and maintain 95%+ accuracy with advanced quantization.

INT8

INT4

FP16

GGUF

GPTQ

AWQ

Optimize Your Model View Deployments

Model Compression

Original Model32GB

Quantized Model32.0GB

Size Reduction

SIZE

32.0GB

SPEED

45ms

ACCURACY

96.8%

📱Run on mobile devices

⚡4x faster inference

💰80% lower costs

🔒On-device privacy

⚠️ THE PROBLEM

LLMs Are Too
Expensive & Slow

A single GPT-4 level model costs $200K+/year to run. Inference is slow. Mobile deployment? Forget it.

💸

$200K+

Annual GPU Costs

For a 70B parameter model

🐌

2-5s

Response Time

Users expect < 1s

📱

80GB

Memory Required

Cannot fit on devices

✓ THE SOLUTION

Quantization Changes Everything

Compress models by 75% without losing accuracy. Deploy anywhere, run 4x faster, pay 90% less.

📦

75%

Size Reduction

⚡

Faster Inference

💰

90%

Cost Savings

🎯

95%+

Accuracy Retained

❌ Before Quantization

Model Size140 GB

Memory Need80 GB VRAM

Inference Time3.2 seconds

Cost per 1M tokens$60

DeploymentCloud only

✓ After Quantization (INT4)

Model Size35 GB (-75%)

Memory Need20 GB VRAM

Inference Time0.8 seconds

Cost per 1M tokens$6 (-90%)

DeploymentCloud + Edge + Mobile

How Quantization Works

The science behind model compression

Original Model (FP32)

Neural networks store weights as 32-bit floating point numbers

Storage

32 bits per weight

Example Value

3.141592653589793

Quantization (INT8)

Convert to 8-bit integers, reducing precision but maintaining range

Storage

8 bits per weight

Example Value

3 (rounded)

Aggressive (INT4)

Further compress to 4-bit integers for maximum compression

Storage

4 bits per weight

Example Value

3 (limited range)

Precision Trade-offs

Precision	Size Reduction	Speed Gain	Accuracy Loss	Best For
FP32 (Original)	0%	1x	0%	Training
FP16	50%	2x	< 0.1%	Cloud inference
INT8	75%	3-4x	< 1%	Production
INT4	87.5%	4-5x	1-3%	Edge/Mobile

Quantization Techniques

INT8 Quantization

Reduction50%

Accuracy99%

Best forGeneral purpose

INT4 Quantization

Reduction75%

Accuracy95%

Best forMobile/Edge

Mixed Precision

Reduction60%

Accuracy98%

Best forBalanced

GPTQ

Reduction70%

Accuracy96%

Best forLLMs

Choosing The Right Precision

Not all quantization is created equal

🔷

FP16 (16-bit Float)

Best for: Cloud deployment with GPU acceleration

Accuracy

99.9%

Speed

2x faster

Memory

50% of original

✓ Advantages

• Minimal accuracy loss
• 2x smaller
• Hardware accelerated

✗ Limitations

• Still needs significant VRAM
• Not suitable for mobile

🟢

INT8 (8-bit Integer)

Best for: Production-grade inference at scale

Accuracy

98-99%

Speed

3-4x faster

Memory

25% of original

✓ Advantages

• 75% size reduction
• 3-4x speed boost
• Wide hardware support

✗ Limitations

• Requires calibration
• Slight accuracy drop

🟣

INT4 (4-bit Integer)

Best for: Edge devices, mobile, embedded systems

Accuracy

95-97%

Speed

4-5x faster

Memory

12.5% of original

✓ Advantages

• 87.5% reduction
• 4-5x faster
• Runs on CPUs

✗ Limitations

• Noticeable accuracy loss
• Complex calibration

Quick Selector

☁️

Cloud Only?

→ Use FP16

Best accuracy, still fast

⚖️

Balanced?

→ Use INT8

Production standard

📱

Edge/Mobile?

→ Use INT4

Maximum compression

Compression Results

Model Size

Before

32GB

→

After

8GB

Inference Time

Before

250ms

→

After

45ms

Memory Usage

Before

40GB

→

After

10GB

Real-World Performance

Actual benchmarks from production systems

Llama 2 70B

Chatbot Assistant

Original (FP32)

Size

140 GB

Time

3.2s

Cost/req

$0.06

INT8 Quantized

Size

35 GB

Time

0.8s

Cost/req

$0.006

75% smaller4x faster90% cheaper

GPT-3.5 Level

Code Generation

Original (FP32)

Size

80 GB

Time

2.1s

Cost/req

$0.04

INT4 Quantized

Size

10 GB

Time

0.5s

Cost/req

$0.003

75% smaller4x faster90% cheaper

Throughput Comparison

FP32 Original100 tokens/sec

100 tok/s

FP16 Half Precision200 tokens/sec

200 tok/s

INT8 Quantized350 tokens/sec

350 tok/s

INT4 Aggressive450 tokens/sec

450 tok/s

Hardware Compatibility

Run quantized models everywhere

☁️

Cloud GPUs

Supported:

NVIDIA A100H100V100T4

Precision:FP16, INT8, INT4

Speed:Fastest

🎮

Consumer GPUs

Supported:

RTX 4090RTX 3090RTX 4080

Precision:INT8, INT4

Speed:Fast

📱

Mobile/Edge

Supported:

Apple M2/M3SnapdragonMediaTek

Precision:INT8, INT4

Speed:Good

💻

CPUs

Supported:

Intel XeonAMD EPYCARM

Precision:INT8, INT4

Speed:Acceptable

Minimum Requirements

Model Size	Original	INT8	INT4
7B params	28 GB	7 GB RTX 3060	3.5 GB iPhone 15
13B params	52 GB	13 GB RTX 3090	6.5 GB M2 MacBook
30B params	120 GB	30 GB A100 40GB	15 GB RTX 4090
70B params	280 GB	70 GB A100 80GB	35 GB A100 40GB

Hardware Acceleration

8x faster

NVIDIA TensorRT

Optimized INT8/FP16 inference on NVIDIA GPUs

5x faster

Apple Neural Engine

Hardware acceleration on iOS/Mac devices

3x faster

ONNX Runtime

Cross-platform quantized inference

Deploy Anywhere

📱

Mobile

iOS & Android

🔲

Edge

Raspberry Pi, Jetson

🌐

Browser

WebAssembly

☁️

Cloud

AWS, GCP, Azure

Industry Benchmarks

Validated results from independent testing

Inference Latency

GPT-J 6B

FP32

2800ms

INT8

700ms

INT4

560ms

Llama 7B

FP32

3200ms

INT8

800ms

INT4

640ms

Mistral 7B

FP32

2900ms

INT8

725ms

INT4

580ms

Cost per 1M Tokens

GPT-J 6B

FP32

$45

INT8

$4.5

INT4

$2.25

Llama 7B

FP32

$52

INT8

$5.2

INT4

$2.6

Mistral 7B

FP32

$48

INT8

$4.8

INT4

$2.4

Accuracy Retention Across Tasks

Text Generation

BLEU score on translation

FP32100%

INT899.2%(-0.8%)

INT496.8%(-3.2%)

Q&A Systems

F1 score on SQuAD

FP32100%

INT898.7%(-1.3%)

INT495.3%(-4.7%)

Classification

Accuracy on GLUE

FP32100%

INT899.5%(-0.5%)

INT497.2%(-2.8%)

Quantization Frameworks

We support all major frameworks

🔥

PyTorch

Excellent Support

Methods:

torch.quantizationFX Graph ModeEager Mode

Key Benefits:

✓Native support
✓Easy to use
✓Good docs

Best for:

Research & Development

🟠

TensorFlow Lite

Excellent Support

Methods:

Post-trainingQATDynamic range

Key Benefits:

✓Mobile-first
✓Cross-platform
✓Optimized

Best for:

Mobile & Edge

⚙️

ONNX Runtime

Good Support

Methods:

Static quantizationDynamic quantization

Key Benefits:

✓Framework agnostic
✓Production-ready
✓Fast

Best for:

Production Deployment

🟢

TensorRT

Excellent Support

Methods:

INT8 calibrationFP16Mixed precision

Key Benefits:

✓Fastest on NVIDIA
✓Auto-optimization
✓Low latency

Best for:

NVIDIA GPUs

🤗

HuggingFace Optimum

Growing Support

Methods:

GPTQGGMLAWQ

Key Benefits:

✓LLM-focused
✓Easy integration
✓Pre-quantized models

Best for:

LLM Deployment

🦙

llama.cpp

Excellent Support

Methods:

GGUF formatk-quantsMixed precision

Key Benefits:

✓CPU-optimized
✓No dependencies
✓Runs everywhere

Best for:

CPU Inference

Quick Integration

🔥PyTorch Example

import torch

# Load model
model = torch.load('model.pth')

# Quantize to INT8
quantized = torch.quantization
  .quantize_dynamic(
    model, {torch.nn.Linear}, 
    dtype=torch.qint8
  )

# 75% smaller, 3x faster!

🤗HuggingFace Example

from optimum.gptq import GPTQQuantizer

# Load and quantize
quantizer = GPTQQuantizer(
  bits=4,
  dataset="c4"
)

# Quantize LLM
quantized_model = quantizer.quantize(
  model, save_dir="./quantized"
)

Advanced Optimization

Beyond basic quantization

⚡

Post-Training Quantization (PTQ)

Quantize pre-trained models without retraining

Easy

< 1 hour

Expected Accuracy

95-98%

Time Required

< 1 hour

Difficulty

Easy

Steps:

1.Load model
2.Calibrate with sample data
3.Convert weights
4.Done!

✓ Pros:

• Fast
• No training needed
• Good for most use cases

✗ Cons:

• Slight accuracy loss
• Limited control

🎯

Quantization-Aware Training (QAT)

Train model with quantization in mind for better accuracy

Medium

1-3 days

Expected Accuracy

98-99.5%

Time Required

1-3 days

Difficulty

Medium

Steps:

1.Insert fake quant nodes
2.Fine-tune for epochs
3.Calibrate ranges
4.Convert

✓ Pros:

• Higher accuracy
• Better quality
• Production-grade

✗ Cons:

• Requires training
• More time
• Needs data

🎨

Mixed Precision

Different precision for different layers (sensitive layers stay FP16)

Advanced

2-5 days

Expected Accuracy

99%+

Time Required

2-5 days

Difficulty

Advanced

Steps:

1.Profile sensitivity
2.Mark layers
3.Selective quantization
4.Fine-tune

✓ Pros:

• Best accuracy
• Optimal performance
• Minimal loss

✗ Cons:

• Complex
• Manual tuning
• Requires expertise

✓ Solution:

Aggressive INT4 quantization + pruning

📊 Results:

< 500KB modelBattery-efficientEdge inference

Examples:

Smart homeWearablesIndustrial sensors

Success Metrics

💰

90%

Cost Reduction

⚡

Faster Inference

📦

75%

Size Reduction

🎯

95%+

Accuracy Retained

Supported Models

We quantize any LLM

🦙

Llama Family

GPT Family

Production-Ready

Variants:

• GPT-J 6B

• GPT-NeoX 20B

• GPT4All

• Dolly

• StableLM

Quantization Support

FP16, INT8, INT4

⚡

Mistral / Mixtral

Trending

Variants:

• Mistral 7B

• Mixtral 8x7B

• Zephyr

• OpenChat

Quantization Support

FP16, INT8, INT4

Complete Model Support

Model	Parameters	Original Size	INT8 Size	INT4 Size	Status
Llama 2	7B	13 GB	7 GB	4 GB	Tested
Llama 2	13B	26 GB	13 GB	7 GB	Tested
Llama 2	70B	140 GB	70 GB	35 GB	Tested
Mistral	7B	14 GB	7 GB	4 GB	Tested
Mixtral	8x7B	90 GB	45 GB	23 GB	Tested
GPT-J	6B	12 GB	6 GB	3 GB	Tested
Falcon	7B-180B	Varies	Yes	Yes	Supported
Your Custom Model	Any	Any	✓	✓	Contact Us

DIY vs Professional

Why hire us?

❌ DIY Quantization

Figure it out yourself

😩

Trial & Error

Weeks of experimentation

📉

Accuracy Loss

5-10% degradation common

🐌

No Optimization

Suboptimal performance

🔧

Format Issues

Compatibility problems

🐛

Hidden Bugs

Production failures

😰

No Support

You are on your own

2-4 weeks

Your time wasted

✓ TensorBlue Quantization

Professional service

🎯

Expert Optimization

95%+ accuracy guaranteed

⚡

Fast Turnaround

2-5 days delivery

✅

Production-Ready

Tested & validated

📦

Multi-Format

ONNX, TensorRT, GGUF

🛡️

Full Support

90-day guarantee

📚

Documentation

Complete guides included

2-5 days

Professional delivery

ROI Calculator

Your Engineer's Time

$15K

2 weeks @ $150/hr

Our Service

$5K

Fixed price, 2-5 days

You Save

$10K

+ better results

Return on Investment

Quantization pays for itself in weeks

❌ Before Quantization

$18K/mo

Running costs

GPU rental (A100 x2)$12,000

API calls (1B tokens)$5,000

Infrastructure$1,000

Annual Cost$216K

✓ After Quantization

$2K/mo

Running costs

GPU rental (T4 x1)$500

API calls (1B tokens)$500

Infrastructure$1,000

Annual Cost$24K

Annual Savings Breakdown

💰

$192K

Total Savings

📊

89%

Cost Reduction

⚡

Faster Inference

📅

9 days

ROI Payback

Investment Payback Timeline

Quantization Service-$5,000

Monthly Savings+$16,000

✓ Break-even in 9 days!

Beyond Cost Savings

🚀

Faster Time to Market

Deploy in days, not months

⚡

Better User Experience

Sub-second responses

🏆

Competitive Advantage

Offer AI at lower prices

STARTING FROM

$10K

✓2-4 week delivery

✓Multiple formats

✓Performance testing

✓2 months support

Get Custom Quote

Duration

Ongoing

Tasks:

✓Model delivery

✓Integration guide

✓Knowledge transfer

✓90-day support

Deliverable:Complete package + support

Our Guarantees

Risk-free quantization service

🎯

Accuracy Guarantee

95%+ Accuracy Retention

We guarantee your quantized model maintains at least 95% of original accuracy. If we fall short, we refund 100%.

Includes:

✓Validated on your data

✓Extensive testing

✓Performance reports

⚡

Speed Guarantee

3x Faster Minimum

Your quantized model will be at least 3x faster than the original, or we keep working until it is—no extra cost.

Includes:

✓Real-world benchmarks

✓Production environment

✓Latency optimization

🛡️

Support Guarantee

90-Day Free Support

Full technical support for 90 days post-delivery. Bug fixes, optimization tweaks, and integration help included.

Includes:

✓Email & Slack support

✓Response within 24h

✓Unlimited questions

💰

Money-Back Guarantee

100% Refund If Unsatisfied

Not happy with the results? Get a full refund within first 7 days. No questions asked. We are that confident.

Includes:

✓7-day trial period

✓No lock-in

✓Full transparency

Why Trust Us?

📦

100+

Models Quantized

⭐

98%

Client Satisfaction

✅

Failed Projects

💬

< 24h

Support Response

Frequently Asked Questions

Everything you need to know

Still Have Questions?

Talk to a quantization expert. Free 30-minute consultation.

Optimize Your
AI Models

Reduce size by 75% while maintaining 95%+ accuracy

Start Compression View Results

CompressLLMs by75%

Model Compression

LLMs Are TooExpensive & Slow

Quantization Changes Everything

❌ Before Quantization

✓ After Quantization (INT4)

How Quantization Works

Original Model (FP32)

Quantization (INT8)

Aggressive (INT4)

Precision Trade-offs

Quantization Techniques

INT8 Quantization

INT4 Quantization

Mixed Precision

GPTQ

Choosing The Right Precision

FP16 (16-bit Float)

INT8 (8-bit Integer)

INT4 (4-bit Integer)

Quick Selector

Compression Results

Real-World Performance

Llama 2 70B

GPT-3.5 Level

Throughput Comparison

Hardware Compatibility

Cloud GPUs

Consumer GPUs

Mobile/Edge

CPUs

Minimum Requirements

Hardware Acceleration

Deploy Anywhere

Mobile

Edge

Browser

Cloud

Industry Benchmarks

Inference Latency

Cost per 1M Tokens

Accuracy Retention Across Tasks

Text Generation

Q&A Systems

Classification

Quantization Frameworks

PyTorch

TensorFlow Lite

ONNX Runtime

TensorRT

HuggingFace Optimum

llama.cpp

Quick Integration

Advanced Optimization

Post-Training Quantization (PTQ)

Quantization-Aware Training (QAT)

Mixed Precision

Cutting-Edge Techniques

GPTQ

AWQ

SmoothQuant

GGML/GGUF

Real-World Use Cases

Mobile Apps

Healthcare

E-commerce

Autonomous Vehicles

Gaming

IoT Devices

Success Metrics

Supported Models

Llama Family

GPT Family

Mistral / Mixtral

Complete Model Support

DIY vs Professional

❌ DIY Quantization

✓ TensorBlue Quantization

ROI Calculator

Return on Investment

Compress
LLMs by
75%

LLMs Are Too
Expensive & Slow

Optimize Your
AI Models