What is Quantization?
A technique to reduce model memory usage by representing weights in lower precision (INT8, INT4, GGUF-Q4). Quantization trades a small accuracy loss for significant VRAM reduction.
Related Calculators
AI VRAM
Calculate the GPU VRAM required to run any LLM locally or on cloud GPU. Supports all quantization levels — FP32, FP16, INT8, INT4, and GGUF variants.
Llama 3 70B VRAM
Calculate the exact GPU VRAM needed to run Meta Llama 3 70B. At FP16 it needs 140 GB — but INT4 quantization brings it down to ~35 GB, fitting on a single A100 40 GB.
DeepSeek R1 VRAM
Calculate VRAM for DeepSeek R1 (671B MoE) and its distilled variants (1.5B–70B). The full R1 requires massive multi-GPU setups; distilled versions run on consumer hardware.
Related Comparisons
RunPod vs Lambda Labs
RunPod vs Lambda Labs for GPU cloud — compare A100/H100 on-demand pricing, availability, spot instances, and which is better for LLM inference and fine-tuning.
RunPod vs Vast.ai
RunPod vs Vast.ai — GPU rental marketplace comparison. Vast.ai offers lower spot prices but variable reliability; RunPod offers higher consistency. Which is right for your LLM workload?