How Much VRAM for a 70B Model?

Quick Answer

A 70B model at Q4_K_M needs approximately 40 GB of VRAM. Consumer options: dual RTX 3090 (48 GB total), M5 Max with 128 GB unified memory, or cloud GPU rental.

▸Q4_K_M 70B: ~40 GB VRAM required
▸Dual RTX 3090 (48 GB total): consumer desktop option
▸M5 Max 128 GB unified memory: best single-machine experience

Updated: 2026-05

Quantization & VRAMAdvanced

Key Takeaways

✓A 70B model at Q4_K_M needs approximately 40 GB of VRAM
✓Consumer hardware options: dual RTX 3090 (48 GB) or Apple M5 Max with 128 GB unified memory
✓For occasional use under 5 hours per week, cloud GPU rental at $0.50–$1.50/hour is cheaper than buying hardware

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.

Hardware	Total VRAM	Speed
Dual RTX 3090	48 GB	~8 tok/s
RTX 3090 + CPU offload	24 GB + 32 GB RAM	~3 tok/s
Apple M5 Max 128 GB	128 GB unified	~15 tok/s
RunPod H100 (cloud)	80 GB	~50 tok/s

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.

Quick Answers About 70B Model VRAM

Can a single RTX 3090 run a 70B model?▾

Partially. A single RTX 3090 (24 GB) can run 70B with CPU offload, but speed drops to ~3 tok/s — too slow for interactive use. Full GPU inference for 70B requires 40+ GB in combined VRAM.

Can I run a 70B model on a MacBook?▾

Only on M3 Max, M4 Max, M4 Ultra, or M5 Max with 128 GB of unified memory. A MacBook with 32 GB RAM cannot run 70B at Q4. See the RAM sizing guide for smaller model alternatives.

Is there a cheaper way to run 70B models locally?▾

Yes — use Q2_K quantization to bring the 70B model down to ~21 GB VRAM, but quality degrades significantly. Alternatively, 34B models at Q5 deliver 80–90% of 70B quality at half the VRAM requirement.

How does 70B VRAM compare to a 13B model?▾

A 13B model at Q4 needs ~9 GB VRAM vs ~40 GB for 70B. For most tasks — chat, coding, summarization — a 13–14B model at Q5 covers the gap. See VRAM requirements by model size.

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