What is the cheapest way to run 70B locally?

Two used 24 GB cards (2x RTX 3090) give you 48 GB of VRAM for far less than a single 48 GB workstation card, at the cost of higher power draw and a motherboard with two PCIe slots.

Best GPU to Run Llama 3 70B Locally (2026)

Q: Can a single RTX 4090 run Llama 3 70B?

Not fully. A 24 GB RTX 4090 holds roughly half of a 70B model at Q4 (~48 GB needed). You can run it with CPU/RAM offload, but expect low single-digit tokens per second. For a comfortable all-GPU experience you want ~48 GB of VRAM.

Q: How much VRAM does Llama 3 70B need?

About 38–39 GB for the weights at 4-bit, plus KV cache and overhead, landing near 48 GB total at an 8K context. At 8-bit it roughly doubles to ~75 GB.

Llama 3.3 70B is the model people actually mean when they say they want a "serious" local LLM. It reasons like a frontier model from a year ago and runs entirely on your hardware. The catch is one number: roughly 48 GB of VRAM to run it comfortably at 4-bit. That number decides everything else.

The math, in one paragraph

A 70.6B-parameter model at 4-bit quantization needs about 70.6 × 0.55 ≈ 38.8 GB just for the weights. Add the KV cache for an 8K context (around 8.4 GB on a model this size) and ~2 GB of runtime and OS overhead, and you land near 48–49 GB total. Push the context to 32K and the KV cache alone climbs past 30 GB. This is why "will it fit?" is the only question that matters for 70B.

Key takeaway 70B at Q4 ≈ 48 GB of VRAM. That rules out every single consumer card except as a partial, offloaded run. You either buy 48 GB of VRAM, or you split it across two cards, or you move to unified memory.

The options that actually work

Setup	VRAM	70B Q4?	Notes
2× RTX 3090 / 3090 Ti	48 GB	Yes	Best value. Used cards, high power draw, needs 2 PCIe slots.
2× RTX 4090	48 GB	Yes	Faster, pricier, hot. NVLink not required for inference.
2× RTX 5090	64 GB	Yes (roomy)	Headroom for long context or Q8. Expensive.
1× RTX 6000 Ada / Pro	48 GB	Yes	Single-card simplicity, workstation price.
1× RTX 4090 / 5090	24 / 32 GB	Partial	Offloads to system RAM — usable but slow (low single-digit tok/s).
Apple M-series, 64 GB+	Unified	Yes	Unified memory fits it; ~10–20 tok/s on M3/M4 Max class.

Our recommendation

If you want the cheapest path to real 70B speed, two 24 GB cards (a pair of used RTX 3090s) is still the value king in 2026 — 48 GB of pooled VRAM for less than one workstation card. If you hate the noise, heat, and dual-PSU-curious power draw, a 64 GB+ Apple Silicon Mac runs the same model silently on a fraction of the wattage, just slower. A single 24 GB card can technically load it with offload, but you'll spend more time waiting than reading.

If 48 GB feels like a lot to commit to one model, remember you can drop to a 70B model at a more aggressive quant, or step down to a 32B-class model that fits comfortably in 24 GB. That trade-off — size vs. quant vs. context — is exactly what the calculator below maps out for any model you pick.

Find the exact GPU for your model

Pick Llama 3 70B (or any model), choose your quantization and context, and get a fits / tight / needs-offload verdict with the full VRAM breakdown.

Open the Local AI Calculator →

FAQ

Can a single RTX 4090 run Llama 3 70B?

Not fully. A 24 GB 4090 holds about half the model at Q4, so the rest spills to system RAM and the GPU waits on the CPU. It runs, but at low single-digit tokens per second. For an all-GPU experience you want ~48 GB.

How much VRAM does Llama 3 70B need?

~38.8 GB for weights at 4-bit, plus KV cache and overhead — about 48 GB total at 8K context. At 8-bit it roughly doubles to ~75 GB.

Is the 405B model worth it over 70B?

For most people, no. Llama 3.1 405B needs hundreds of GB of memory and a multi-GPU server. 70B captures the vast majority of the quality for a fraction of the hardware.

We may partner with companies or groups to affiliate hardware products based on user needs, earning a commission from qualifying purchases. VRAM figures are reproducible estimates (weights + KV cache + overhead) and may vary by runtime, batch size, and quant format. Data current as of June 2026.