Can I run Llama 3 8B on an RTX 3060 12GB?

Yes! If you use 4-bit quantization (QLoRA for training or GGUF for inference), it fits comfortably in under 8GB VRAM. For full BF16 training, you'll hit memory limits, but for inference and lightweight fine-tuning, the 3060 is surprisingly capable.

Do I need an A100 for Llama 3 70B?

For full parameter fine-tuning? Yes, absolutely (probably 4-8 of them). But for inference or QLoRA fine-tuning? No. You can run 70B (4-bit quantized) on two RTX 3090s/4090s (24GB each) using NVLink or simple model parallelism. It's much cheaper than renting an A100.

How much VRAM does Llama 3 70B need for inference?

At full precision (FP16), you need about 140GB VRAM, so 2x A100 80GB. But nobody runs raw FP16 inference anymore. With 4-bit quantization, it shrinks to ~40GB. This fits perfectly on a single A6000 (48GB) or split across two consumer 24GB cards.

Is Mac Studio (M2/M3 Ultra) good for Llama 3?

Actually, yes. The unified memory (up to 192GB) is huge. It's slower than NVIDIA GPUs for training (no CUDA), but for running local inference on 70B models, it's incredible. I've used an M2 Ultra with 128GB RAM and it handles 70B Q4_K_M effortlessly.

Llama 3 VRAM Requirements: I Tested 8B and 70B on Different GPUs (2026)

My Test Rig:

• Local: 2x RTX 4090 (24GB each) w/ NVLink bridge
• Cloud: Lambda Labs 1x A100 (80GB) & 8x H100 (80GB)
• Software: PyTorch 2.2, Hugging Face Transformers, Bitsandbytes (for quantization)

Llama 3 is a beast. The 70B model especially is a significant leap over Llama 2. But unlike the old days where "big model = datacenter only," we have better tools now. Quantization, LoRA, and Flash Attention 3 have changed the math.

I spent the last 48 hours running different configurations. I crashed my local machine five times (OOM errors are my lullaby), but I got the numbers.

The 8B Model: The Consumer Sweet Spot

The 8B model is surprisingly capable and fits almost anywhere. If you have a modern GPU, you're probably good.

Inference (Running the model)

Full Precision (FP16/BF16): ~16GB VRAM. Fits on RTX 3090/4090, 4080 (16GB), and A100/A10.
4-bit Quantized (Q4_K_M): ~6GB VRAM. This is the magic number. It runs on RTX 3060, 4060, even some laptop GPUs.

Training (Fine-tuning)

Here's where it gets tricky.

Full Fine-tune: Don't bother on consumer cards. You need ~60-80GB VRAM because of optimizer states (AdamW adds 2x params + gradients). You need an A100 80GB.
LoRA / QLoRA: This is what you want. With QLoRA (4-bit base model + adapters), I trained Llama 3 8B on a single RTX 4090 using just 14GB VRAM. It was fast, stable, and the results were 95% as good as a full fine-tune.

My Take: For 8B, the RTX 3090/4090 is the king. It's cheap, fast, and has 24GB VRAM, giving you plenty of headroom for batch sizes or longer context windows (8k+).

The 70B Model: The VRAM Eater

This is what everyone wants to run. The 70B model rivals GPT-4 in some benchmarks, but it's heavy.

Inference

Full Precision (FP16): ~140GB VRAM. You need 2x A100 80GB cards. Costly (~$3-4/hr).
4-bit Quantized: ~40GB VRAM. This is the sweet spot. It just misses fitting on a single 3090/4090.

The "Dual 3090" Hack:
This is my favorite setup. I bought two used RTX 3090s ($700 each on eBay) and put them in one PC. With `llama.cpp` or `vLLM` using tensor parallelism, I have 48GB VRAM total.

Llama 3 70B (4-bit) loads in ~38-40GB. It runs at ~15-20 tokens/second split across the two cards. It's insanely cost-effective compared to cloud rentals if you run it 24/7.

Training (70B)

Forget consumer cards. Even with QLoRA, a 70B model needs ~48-60GB VRAM to train comfortably with a decent context length.

I tried QLoRA on my dual 4090 setup. It technically worked with extreme gradient checkpointing and batch size 1, but it was painfully slow.

The Solution: I rented 4x A100 80GBs for 6 hours ($40 total). I finished the epoch in no time. For 70B training, just pay the cloud tax. It's cheaper than your time (and electricity).

Summary Table: What GPU Do You Need?

Task	Minimum VRAM	Recommended GPU	Budget Option
Llama 3 8B (Inference)	6 GB (4-bit)	RTX 4060 Ti / 3060	RTX 2060 / Laptop
Llama 3 8B (Fine-tune)	16 GB (LoRA)	RTX 3090 / 4090 (24GB)	RTX 4080 (16GB)
Llama 3 70B (Inference)	40 GB (4-bit)	RTX 6000 Ada / A6000	2x RTX 3090 (Used)
Llama 3 70B (Fine-tune)	80 GB (QLoRA)	A100 80GB / H100	Cloud Rental (~$2/hr)

Conclusion: Buy or Rent?

If you're just playing with 8B, buy a 3090 or 4090. The 24GB VRAM is a superpower that will last you years.

If you want to run 70B locally, look into the dual-GPU route (2x 3090/4090). It's fun to build and works surprisingly well.

But if you need to train 70B, don't be a hero. Check the live prices on our tracker. You can often grab an A100 for under $1.50/hr on spot pricing. Renting for a few hours is way cheaper than buying $30,000 worth of hardware you'll only use occasionally.