What GPU do I need to run OpenGVLab/InternVL3-78B?
A 78.4B vision-language model that reads images alongside text. 78.4B parameters, published in BF16. View on Hugging Face
InternVL3-78B is published by OpenGVLab on Hugging Face, with 13,741 downloads and 239 likes to date. It's a InternVLChatModel model built for image-text-to-text, published natively in BF16.
What InternVL3-78B is
InternVL3-78B is a 78.4B-parameter vision-language model published by OpenGVLab (Shanghai AI Lab) on Hugging Face. It is released under Custom license.
License note: a lab-specific license (tagged "other" on Hugging Face); read the model card's own license section before commercial use. Facts in this section are sourced from InternVL3-78B's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Image understanding and captioning
- Visual question answering
- Document/OCR-style reading
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 146.0 GB | 175.3 GB | RTX A5000 | 8 | $1.41/hr |
| FP8 (quantized) | 73.0 GB | 87.6 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 6 | $0.660/hr | ||
| INT4 (quantized) | 36.5 GB | 43.8 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run InternVL3-78B at its published (BF16) precision: 8× RTX A5000, at $0.176/hr per GPU ($1.41/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
InternVL3-78B: common questions
Can InternVL3-78B run on a single GPU?
No. At BF16 it needs 175.3 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest capable card in the live feed is a 24.0 GB RTX A5000, and it takes 8 of them.
How many GPUs do I need to run InternVL3-78B?
8 at BF16. It needs 175.3 GB of VRAM and the cheapest capable live offer is a 24.0 GB RTX A5000, so 8 of them come to $1.41/hr in total.
Does quantizing InternVL3-78B lower the GPU bill?
Yes. At BF16 the cheapest live fit is 8 RTX A5000 cards at $1.41/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
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 A5000 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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More InternVL3 models
- InternVL3-8B (7.9B, BF16)
- InternVL3-1B (938M, BF16)
- InternVL3-1B-hf (938M, BF16)
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
