OPTIMIZATIONModel Efficiency & DeploymentML Calculator
📦

Model Quantization Tradeoffs

Compare GPTQ, AWQ, and GGUF quantization. From Llama 70B INT4 to Mistral GGUF. Calculate compression ratio, memory saved, and speedup by method.

Concept Fundamentals
FP16 → INT8/INT4
Bit Reduction
Precision trade-off
GPTQ / AWQ / GGUF
Methods
Quantization techniques
2–4× smaller
Compression
Model size reduction
Size vs accuracy
Trade-off
Quality loss minimal
CalculateUse the calculator below to run neural computations

Why This ML Metric Matters

Why: Quantization reduces model size and speeds inference. Tradeoffs: higher compression → more savings but potential accuracy loss.

How: Compressed size = params × bytes per param. Compression ratio = original/compressed. Memory saved = (original - compressed) / original.

  • ●INT4 halves memory vs INT8
  • ●AWQ best at INT4
  • ●70B INT4 fits on 48GB
  • ●FP8 ideal on H100
Sources:Frantar et al. 2022 - GPTQ: Accurate Post-Training QuantizationLin et al. 2023 - AWQ: Activation-aware Weight Quantization
📦
GPTQ · AWQ · GGUF

Compare Quantization Methods — Memory, Speed, Accuracy

From Llama 70B INT4 to Mistral GGUF. Calculate compression ratio, memory saved, and speedup by method.

GPU VRAM →LoRA →

📊 Quick Examples — Click to Load

Inputs

e.g., 7 for 7B
typical 0.5–3%
quant-tradeoff.sh
CALCULATED
Original
13.04 GB
Compressed
3.26 GB
Ratio
4.00×
Memory Saved
9.78 GB
Saved %
75.0%
Speedup
~2.0×
Accuracy
98.5%
Share:
Model Quantization Summary
Compression: 4.00×
75% Memory Saved
7B FP16→INT4AWQ→3.26 GB~2.0× speedup
numbervibe.com/calculators/machine-learning/model-quantization-calculator

Memory Comparison (GB)

Accuracy vs Compression

For educational and informational purposes only. Verify with a qualified professional.

🤖 AI & ML Facts

📊

GPTQ (Frantar 2022) uses layer-wise Hessian-based calibration — one of the first post-training INT4 methods for LLMs

— GPTQ

⚡

AWQ protects 1% of salient weights in FP16 — often matches FP16 quality at INT4

— AWQ

🦙

70B FP16→INT4 fits on a single 48GB GPU — quantization enables consumer hardware inference

— Deployment

🎯

FP8 on H100 achieves 2× memory reduction with <0.5% accuracy drop — no calibration needed

— TensorRT

Precision and method table — planning defaults (March 2026)

Bytes/param are idealized; real checkpoints include overhead. Speedups are illustrative vs FP16 baselines on modern kernels.

PrecisionBytes/param (ideal)
FP162
INT81
INT4 / NF40.5
FP81

📋 Key Takeaways

  • • INT4 halves memory vs INT8; INT8 halves vs FP16 — compression ratio = bytes_orig / bytes_target
  • • AWQ typically preserves accuracy better than GPTQ at same bit-width (activation-aware)
  • • GGUF (llama.cpp) offers flexible Q4_K_M, Q5_K_M formats — great for CPU inference
  • • FP8 (TensorRT) gives near-FP16 accuracy with 2× memory reduction — ideal for H100
  • • Tradeoff: higher compression → more speed/memory savings but potential accuracy loss

💡 Did You Know

📊GPTQ (Frantar 2022) uses layer-wise Hessian-based calibration — one of the first post-training INT4 methods for LLMs
⚡AWQ protects 1% of salient weights in FP16 — often matches FP16 quality at INT4
🦙llama.cpp GGUF format supports mixed quantization (Q4_K_M) — different block sizes for better quality
🎯FP8 on H100 achieves 2× memory reduction with &lt;0.5% accuracy drop — no calibration needed
📐NF4 (4-bit NormalFloat) is used in QLoRA — optimized for normally distributed weights
🔧RTN (Round-to-Nearest) is the simplest quantization — no calibration, fastest but lower quality
📈70B FP16→INT4 fits on a single 48GB GPU — quantization enables consumer hardware inference
🤖vLLM, TensorRT-LLM, and llama.cpp all support GPTQ/AWQ/GGUF for production deployment

📖 How It Works

1. Bytes per Parameter

FP32=4, FP16/BF16=2, INT8=1, INT4/NF4=0.5, FP8=1. Model size = params × bytes.

2. GPTQ

Layer-wise quantization with Hessian-based calibration. Good INT4/INT8 quality. Used in AutoGPTQ.

3. AWQ

Activation-aware: protects important weights. Often better accuracy than GPTQ at same bit-width.

4. GGUF

llama.cpp format. Q4_K_M, Q5_K_M use mixed block sizes. Great for CPU and edge deployment.

5. Speedup Estimates

Method-dependent: AWQ ~2×, GPTQ ~1.8×, GGUF ~1.6×, RTN ~1.4× vs FP16. Actual speed depends on hardware.

🎯 Expert Tips

Prefer AWQ for INT4

AWQ typically preserves accuracy better. Use when quality matters more than speed.

GGUF for CPU/edge

llama.cpp + GGUF runs on CPU, Mac M-series, and Raspberry Pi. Q4_K_M is a good default.

FP8 on H100

If you have H100, FP8 gives 2× memory with minimal accuracy loss. No calibration.

INT8 for safety

When accuracy is critical, INT8 is a safe choice — usually <1% loss.

⚖️ Method Comparison

MethodTypical UseSpeedupAccuracyCalibration
GPTQINT4/INT8, AutoGPTQ~1.8×GoodHessian-based
AWQINT4, vLLM~2×Best at INT4Activation-aware
GGUFCPU, llama.cpp~1.6×GoodBlock-wise
RTNFP8, TensorRT~1.4×Near FP16None

❓ Frequently Asked Questions

GPTQ vs AWQ — which is better?

AWQ typically preserves accuracy better at INT4 by protecting important weights. GPTQ is widely supported and often faster to quantize. For best quality at INT4, prefer AWQ.

What is GGUF Q4_K_M?

GGUF is the format used by llama.cpp. Q4_K_M uses 4-bit quantization with mixed block sizes (K-quants) for better quality. Good default for CPU inference.

When to use FP8?

FP8 is ideal on H100 GPUs. It gives 2× memory reduction with minimal accuracy loss and requires no calibration. Not supported on older GPUs.

How much accuracy do I lose with INT4?

Typically 1–3% on perplexity/benchmarks. AWQ often keeps it under 2%. Task-specific fine-tuning can recover some loss.

Can I run 70B on a single GPU?

Yes. 70B FP16 needs ~140GB. With INT4 (~35GB), it fits on a single 48GB A6000 or A100.

What is NF4?

4-bit NormalFloat — optimized for normally distributed weights. Used in QLoRA and bitsandbytes. Similar size to INT4.

Does quantization speed up inference?

Yes. Fewer bytes = less memory bandwidth. Typical speedups: INT4 ~1.6–2×, INT8 ~1.3–1.5× vs FP16.

How accurate is this calculator?

Size estimates are exact (params × bytes). Speedup and accuracy loss are typical ranges — actual values depend on model, hardware, and calibration.

📊 Quantization by the Numbers

4×
FP16→INT4
2×
FP16→INT8
35GB
70B INT4
2022
GPTQ Paper

⚠️ Disclaimer: This calculator provides estimates for educational and planning purposes. Memory size is exact (params × bytes). Speedup and accuracy loss are typical ranges — actual values depend on model architecture, calibration data, hardware (GPU/CPU), and framework (vLLM, llama.cpp, TensorRT). For production, validate with your specific model and deployment target.

👈 START HERE
⬅️Jump in and explore the concept!
AI

Related Calculators

Model Distillation Size Calculator

Plan teacher-to-student model compression. Calculate size ratios, expected accuracy retention, and training tokens needed.

Machine Learning

Activation Memory Calculator

Estimate activation memory with and without gradient checkpointing. Based on NVIDIA selective recomputation research.

Machine Learning

Context Window Scaling Cost Calculator

Analyze quadratic attention scaling costs. Compare standard vs Flash Attention memory and throughput at different context lengths.

Machine Learning

Gradient Accumulation Steps Calculator

Calculate accumulation steps to achieve target effective batch size on limited GPU memory. Based on DeepSpeed ZeRO research.

Machine Learning

Inference Throughput & Latency Calculator

Estimate tokens/sec, time-to-first-token, and inter-token latency for LLM serving on various GPU configurations.

Machine Learning

KV Cache Size Estimator

Calculate KV cache memory for LLM inference with MHA, MQA, and GQA attention types. Based on PagedAttention research.

Machine Learning