ai-gpu
Llama 3 70B VRAM Calculator
Calculate the exact GPU VRAM needed to run Meta Llama 3 70B. At FP16 it needs 140 GB — but INT4 quantization brings it down to ~35 GB, fitting on a single A100 40 GB.
Running Llama 3 70B: GPU Requirements and Options
Meta Llama 3 70B is the most capable open-weight model in the Llama 3 family. Its 70 billion parameters make it a serious engineering challenge to run efficiently.
VRAM by Quantization
| Quantization | VRAM needed | Minimum GPU |
|---|---|---|
| FP16 | ~140 GB | 2× A100 80 GB |
| INT8 | ~70 GB | A100 80 GB (tight) |
| INT4 / GGUF Q4 | ~35 GB | A100 40 GB or H100 |
| GGUF Q4_K_M | ~38 GB | A100 40 GB |
Deployment Options
- ›llama.cpp: Runs GGUF Q4 on a single A100 40GB, or CPU+GPU split for lower VRAM
- ›vLLM: Best throughput for production inference, supports AWQ/GPTQ INT4
- ›Ollama: Simplest setup for local use with GGUF models
- ›TGI (Text Generation Inference): Production-ready, supports multi-GPU
Cloud Cost for 70B
At GGUF Q4 on a single A100 40GB (RunPod ~$1.89/hr):
- ›8 hrs/day × 22 days = $333/mo
- ›Reserved 24/7 on Lambda Labs: ~$930/mo (A100 40GB reserved)
Use the [GPU Hosting Cost Calculator](/calculators/gpu-hosting-cost-calculator) to compare providers.
Frequently Asked Questions
What GPU can run Llama 3 70B at full precision (FP16)?
FP16 requires ~140 GB VRAM. You need 2× A100 80 GB (NVLink), 4× A100 40 GB, or 6× RTX 4090 with tensor parallelism via vLLM or llama.cpp. Cloud: 2× A100 80GB on RunPod costs ~$5/hr.
Can I run Llama 3 70B on a single GPU?
At INT4 (GGUF Q4_K_M), Llama 3 70B requires ~35–38 GB VRAM. It fits on a single A100 40 GB or a single H100 80 GB. At INT4, quality is slightly degraded but remains excellent for most tasks.
How does Llama 3 70B compare to Llama 2 70B in VRAM?
Nearly identical VRAM requirements — both are 70B parameter models. Llama 3 70B uses a 128K token vocabulary vs Llama 2's 32K, which adds ~0.5 GB for the embedding layer, but otherwise VRAM is the same at each quantization level.
What quantization is recommended for Llama 3 70B?
GGUF Q4_K_M is the sweet spot — minimal quality loss, fits on an A100 40GB, and runs well with llama.cpp or Ollama. For production inference via vLLM or TGI, use INT4 (GPTQ or AWQ format). FP16 is only worth it for benchmarking or fine-tuning.