Vision
How to deploy InternVL3-78B on a GPU cloud
A 78.4B vision-language model that reads images alongside text. Full specs, license and use cases.
InternVL3-78B size and hardware requirements
78.4B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
175.3 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 146.0 GB | 175.3 GB | RTX 3090 | 8 | $1.18/hr |
| FP8 (quantized) | 73.0 GB | 87.6 GB | RTX PRO 6000 | 1 | $1.64/hr |
| INT4 (quantized) | 36.5 GB | 43.8 GB | RTX A6000 | 1 | $0.330/hr |
How to run InternVL3-78B
Run InternVL3-78B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve OpenGVLab/InternVL3-78B --tensor-parallel-size 8Run InternVL3-78B with GGUF quantizations
Prebuilt GGUF weights published at unsloth/InternVL3-78B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf unsloth/InternVL3-78B-GGUFDeploy InternVL3-78B on Aquanode
Aquanode has no one-click deploy template for InternVL3-78B; 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 (8× 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