How Much RAM Do You Need for Local AI?

RAM questions for local AI get confusing fast because there are two completely different memory pools involved: the VRAM on your discrete GPU, and the system RAM in your motherboard. They have different roles and different minimum requirements. This guide separates them clearly and gives you the actual numbers for each tier.

VRAM vs system RAM: what each one does

VRAM (Video RAM) is the memory on your GPU. When you run a model with GPU acceleration, the model weights are loaded into VRAM. This is the primary bottleneck — if the model does not fit, it either runs on CPU (slow) or fails entirely. System RAM (what people usually mean when they say "RAM") sits on your motherboard. It handles your OS, applications, and the parts of a model that overflow from VRAM when you use CPU offloading.

8 GB VRAM — minimum, tiny models only

With 8 GB of VRAM you can run 7B models at 4-bit quantization (they need 4-5 GB) with about 3 GB left for context. That covers Llama 3.1 8B, Mistral 7B, Gemma 2 9B, and Phi-3.5 Mini. You cannot run 13B models without offloading some layers to CPU, which cuts tokens per second dramatically. For image generation, 8 GB handles Stable Diffusion 1.5 and SDXL at 512px, but struggles with SDXL at full resolution and fails on Flux.1.

16 GB VRAM — comfortable for 7B and 13B

16 GB is the sweet spot for most personal use in 2026. You can load Llama 3.1 8B, Mistral 7B, or Gemma 2 9B with room for long context windows. Qwen 2.5 14B and CodeLlama 13B also fit comfortably at 4-bit. For image generation, Flux.1 Schnell and Flux.1 Dev run at full resolution, and SDXL with ControlNet works cleanly. The RTX 4060 Ti 16 GB and RTX 4080 Super 16 GB both hit this tier.

24 GB VRAM — 34B models and multi-model setups

With 24 GB you can run 34B models fully on-device at 4-bit (they need around 20 GB) and have 4 GB spare for context. That opens up CodeLlama 34B, Nous Hermes 34B, and Phi-3 Medium 14B with very long contexts. You can also run two smaller models simultaneously — one for chat, one for code completion — a workflow that matters for local IDE setups. The RTX 4090 and AMD RX 7900 XTX are the main discrete options here; the Mac mini M4 Pro with 48 GB far exceeds this.

64 GB unified memory — quantized 70B fully on-device

A Mac Studio M4 Max with 64 GB of unified memory can run Llama 3.3 70B at 4-bit quantization entirely in memory (needs around 40 GB). The model never touches disk once loaded. Tokens per second at 70B on the M4 Max are modest — roughly 10-15 t/s — but the quality is excellent since you are not doing heroic quantization. This is the minimum recommended setup for anyone who genuinely needs 70B quality in a personal machine.

128 GB unified memory — full 70B or multiple large models

The Mac Studio M4 Max with 128 GB and the Mac Pro with M4 Ultra (192 GB or 256 GB options) are the only consumer machines that can run a 70B model at full FP16 precision without quantization. This matters for researchers who need exact model behavior. You can also run two or three 34B models simultaneously and route between them — a setup some local AI power users use for specialized tasks (one model for code, one for writing, one for reasoning).

How much system RAM do you need alongside your GPU?

For a Windows or Linux machine with a discrete GPU, system RAM is secondary but still matters. The rule: have at least as much system RAM as VRAM, and double it if you do CPU offloading or run other apps simultaneously. A PC with a 16 GB GPU should have at least 32 GB of system RAM. A PC with a 24 GB GPU benefits from 64 GB of system RAM, especially if you run quantized 70B models with some layers offloaded to CPU.

• 8 GB system RAM: only viable if you do nothing else while running AI — not recommended
• 16 GB system RAM: minimum for a 16 GB GPU setup with no CPU offloading
• 32 GB system RAM: comfortable for most discrete GPU setups up to 24 GB VRAM
• 64 GB system RAM: recommended if you offload model layers to CPU or run very large models split across GPU and CPU
• 128 GB system RAM: for AI workstations running 70B+ models with substantial CPU offloading