Home / Can I run it? / Llama 3.3 70B / Llama 3.3 70B on dual RTX 3090s

No. Llama 3.3 70B at Q4_K_M needs about 50.2 GB, and dual RTX 3090s have 48.0 GB usable at a ~4k context.

Can I run Llama 3.3 70B on dual RTX 3090s?

Llama 3.3 70B at Q4_K_M needs about 50.2 GB, and dual RTX 3090s have 48.0 GB usable. Two cards add memory, not decode speed; the speed estimate uses one RTX 3090’s 936 GB/s. Meta’s flagship 70B instruct model, the usual target for a high-end local chat box. These are estimates from our formula, not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 50.2 GB

Memory needed

50.2 GB at Q4_K_M, ~4k context

Usable VRAM

48.0 GB of 48 GB VRAM

Est. tokens/sec

n/a does not fit

Llama-3.3-70B-Instruct · GeForce RTX 3090 · Multi-GPU · Dual 3090 build · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

No speed estimate: Llama 3.3 70B does not fit in the 48.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.

Context headroom

Even at a short ~4k context, the weights do not fit. Extending context would only increase the gap.

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

• Run R1 Distill 32B instead (23.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
• To hold Llama 3.3 70B at Q4, you need about 50.2 GB. The MacBook Pro M5 Max 128GB has 96.0 GB usable ($6699 Apple list price, 14-inch M5 Max 18C CPU/40C GPU, 128GB/2TB).
• Our estimate is only 2.2 GB over this machine’s usable memory, so a tighter quant or a shorter context may work in practice. We still mark it as a no under the published formula.
• CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
• See the best GPU for this model family and the Llama-3.3-70B-Instruct model page.

Other hardware for Llama 3.3 70B

• Llama 3.3 70B on an RTX 3060 12GBWon’t fit
• Llama 3.3 70B on an RTX 3070 TiWon’t fit
• Llama 3.3 70B on an RTX 3080Won’t fit
• Llama 3.3 70B on an RTX 3090Won’t fit
• Llama 3.3 70B on an RTX 4060 Ti 16GBWon’t fit
• Llama 3.3 70B on an RTX 4070 Ti SuperWon’t fit

Other models on dual RTX 3090s

• R1 Distill 70B on dual RTX 3090sWon’t fit
• gpt-oss-120b on dual RTX 3090sWon’t fit
• Qwen3-235B on dual RTX 3090sWon’t fit
• DeepSeek-R1 on dual RTX 3090sWon’t fit
• Mixtral 8x22B on dual RTX 3090sWon’t fit
• GLM-4.5-Air on dual RTX 3090sWon’t fit