LLM
How to deploy Llama 3.3 70B Instruct on a GPU cloud
Meta's 70B multilingual instruction-tuned chat model. Full specs, license and use cases.
Llama 3.3 70B Instruct size and hardware requirements
70.6B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
157.7 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 131.4 GB | 157.7 GB | RTX 3090 | 7 | $1.03/hr |
| FP8 (quantized) | 65.7 GB | 78.8 GB | RTX PRO 6000 WS | 1 | $1.42/hr |
| INT4 (quantized) | 32.9 GB | 39.4 GB | RTX A6000 | 1 | $0.330/hr |
How to run Llama 3.3 70B Instruct
Run Llama 3.3 70B Instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve meta-llama/Llama-3.3-70B-Instruct --tensor-parallel-size 7Run Llama 3.3 70B Instruct with Ollama
Verified against Ollama's own library listing.
ollama run llama3.3:70bRun Llama 3.3 70B Instruct with GGUF quantizations
Prebuilt GGUF weights published at unsloth/Llama-3.3-70B-Instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/Llama-3.3-70B-Instruct-GGUFSource: https://huggingface.co/unsloth/Llama-3.3-70B-Instruct-GGUF
Deploy Llama 3.3 70B Instruct on Aquanode
Aquanode has no one-click deploy template for Llama 3.3 70B Instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (7× RTX 3090 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod