How Much VRAM for a 70B Model?

Hardware Options for Running a 70B Model

As of May 2026, a 70B model at Q4_K_M is approximately 40 GB of compressed weights — 1.7× a single RTX 4090 and 1.6× a single RTX 3090. This is why 70B is the hardest tier to run locally: it crosses the boundary between consumer GPUs (max 24 GB) and workstation hardware. Three paths exist, each with different trade-offs.

Apple M5 Max with 128 GB unified memory is the smoothest single-machine option — no PCIe transfer bottleneck between CPU and GPU memory, and macOS manages allocation automatically. Dual RTX 3090s work but require a workstation-class desktop and careful driver configuration.

When Cloud Makes More Sense Than Local

Cloud GPU rental for 70B inference runs $0.50–$1.50 per hour on RunPod and Lambda Labs as of May 2026. A dual RTX 3090 setup costs $1,500–$2,500 in hardware, which amortizes to cloud costs only after 1,500–3,000 hours of use.

For teams or individuals using 70B models fewer than 5 hours per week, cloud rental is both cheaper and easier to maintain. Local 70B is justified for privacy-sensitive use cases (no data leaving your hardware) or sustained high-frequency inference where cloud costs compound quickly. For smaller models that fit on consumer GPUs, see the VRAM tier guide.

For a full breakdown of 70B deployment strategies, see how to run 70B models with 24 GB VRAM.

Related Guides

• ▸How Much VRAM Do You Need for a Local LLM? — VRAM tier table for all model sizes
• ▸Cheapest Way to Run a 70B Model Locally — cost path when hardware exceeds budget
• ▸Local LLM Hardware Guide 2026 — full hardware guide for 70B-capable builds
• ▸Best Local LLMs 2026 — which 70B models are worth the hardware cost