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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. 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 | AMD MI300X | 1 | $2.63/hr |
| cheaper alt. | RTX A5000 | 8 | $1.41/hr | ||
| FP8 (quantized) | 73.0 GB | 87.6 GB | RTX PRO 6000 WS | 1 | $1.62/hr |
| cheaper alt. | RTX 4000 SFF Ada | 5 | $0.990/hr | ||
| INT4 (quantized) | 36.5 GB | 43.8 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX A5000 | 2 | $0.352/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: 1× AMD MI300X, at $2.63/hr per GPU ($2.63/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: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
KV-cache size for InternVL3-78B is not published for this architecture, so no figure is shown.
InternVL3-78B: common questions
Can InternVL3-78B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 175.3 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 192.0 GB AMD MI300X at $2.63/hr.
Does quantizing InternVL3-78B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one AMD MI300X at $2.63/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 1Run 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 (1× AMD MI300X 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)
Alternatives at this size
Other models for image-text-to-text within about a third of InternVL3-78B's 78.4B parameters, from other model lines.
- Llama-4-Scout-17B-16E-Instruct (108.6B, BF16)
- Qwen2.5-VL-72B-Instruct (73.4B, BF16)
- GLM-4.5V (107.7B, BF16)
More on InternVL3-78B
Fits on a 192 GB GPU at BF16: every model that fits in 192 GB.
Fits on a 96 GB GPU at FP8: every model that fits in 96 GB.
Fits on a 48 GB GPU at INT4: every model that fits in 48 GB.
Best vision-language models: how InternVL3-78B ranks against the rest.
Related reading: AMD MI300X pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
