Local LLM Hardware Guide 2026: How Much RAM and VRAM You Really Need
Every local LLM guide says you need a big GPU, but the actual requirements vary 20x depending on model size, quantization, and whether you use CPU or GPU. Here is the honest math.
💡 What You Will Learn
Every local LLM guide says you need a big GPU, but the actual requirements vary 20x depending on model size, quantization, and whether you use CPU or GPU. Here is the honest math.
📜 Table of Contents
The One Equation That Rules Local LLMs
Memory needed ≈ model weights + KV cache + overhead.
- Weights: params x bits / 8. A 7B model at 4-bit = 7e9 x 4 / 8 = ~3.5GB. At 8-bit, ~7GB.
- KV cache: grows with context (see the KV cache guide) - often 1-4GB at 8k-32k context.
- Overhead: runtime + tokenizer + buffering, roughly 0.5-1GB.
The 2026 Hardware Table
| Model | Quant | RAM/VRAM needed | Hardware that works |
|---|---|---|---|
| 1-3B | Q4 | 2-4GB | any modern laptop, CPU only |
| 7-8B | Q4 | 5-8GB | 8GB GPU, or 16GB RAM CPU |
| 7-8B | Q8 | 9-12GB | 12GB GPU |
| 13-14B | Q4 | 9-12GB | 12-16GB GPU |
| 32B | Q4 | 20-24GB | 24GB GPU (3090/4090 class) |
| 70B | Q4 | 40-48GB | 2x24GB, or M-series Max/Ultra |
GPU vs CPU: The Real Trade
- GPU: 10-50x faster generation, but VRAM is expensive and fixed.
- CPU + RAM: slower (see the CPU guide for speeds) but RAM is cheap - a 64GB machine runs 32B models that no consumer GPU fits.
- Hybrid: llama.cpp/Ollama offload some layers to GPU and keep the rest in RAM; a 12GB GPU + 32GB RAM can run 32B models at acceptable speed.
Apple Silicon Reality
Unified memory means the M-series (M1-M5) uses the same RAM for CPU and GPU. A 64GB M-series Mac runs 32B-70B Q4 models with Metal acceleration at 2-3x the speed of similar x86 CPU setups. For local LLMs, this is often the best price-performance in 2026.
Buying Decisions That Matter
- Decide your model class first - 7B, 32B or 70B? Everything else follows.
- RAM/VRAM is the spec that matters - more memory beats more compute cores for local LLMs.
- Quantization is your free upgrade - Q4 halves the requirement of Q8 (see the quantization guide).
- For laptops: 32GB unified memory (Apple) or 32GB RAM (Windows) handles the 7B-14B class comfortably.
- For desktops: a 24GB GPU (RTX 3090 used or 4090) is the 32B-class sweet spot.
The Minimum Viable Setup
Start with what you own: run a 1.5-3B Q4 model on any 8GB machine today (it is genuinely useful for extraction and summarization). Upgrade only when a specific model you need does not fit.
