What GPU do I need to run llava-hf/llava-v1.6-mistral-7b-hf?
7.6B parameters, published in F16. View on Hugging Face
Set up llava-v1.6-mistral-7b-hfllava-v1.6-mistral-7b-hf is published by llava-hf on Hugging Face, with 551,641 downloads and 314 likes to date. It's a LlavaNextForConditionalGeneration model built for image-text-to-text, published natively in F16.
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) |
|---|---|---|---|---|---|
| FP16 | 14.1 GB | 16.9 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 7.0 GB | 8.5 GB | RTX 5060 Ti | 1 | $0.197/hr |
| INT4 (quantized) | 3.5 GB | 4.2 GB | RTX 3070 | 1 | $0.143/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 llava-v1.6-mistral-7b-hf at its published (F16) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
llava-v1.6-mistral-7b-hf: 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 llava-v1.6-mistral-7b-hf is not published for this architecture, so no figure is shown.
llava-v1.6-mistral-7b-hf: common questions
Does llava-v1.6-mistral-7b-hf fit on a 24 GB GPU?
Yes. At FP16 it needs 16.9 GB of VRAM, so a 24 GB card holds it with 7.1 GB to spare. A 16 GB card is not enough for it at FP16.
What is the least VRAM llava-v1.6-mistral-7b-hf can run in?
4.2 GB, at INT4 (quantized), which fits a 6 GB card, against 16.9 GB at FP16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing llava-v1.6-mistral-7b-hf lower the GPU bill?
Yes. At FP16 the cheapest live fit is one RTX A5000 at $0.176/hr. At INT4 (quantized) it drops to one RTX 3070 at $0.143/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More LLaVA models
- llava-v1.6-mistral-7b (7.6B, BF16)
- llava-med-v1.5-mistral-7b (7.6B, BF16)
- llava-onevision-qwen2-7b-ov (8.0B, BF16)
- LLaVA-Video-7B-Qwen2 (8.0B, BF16)
- llava-1.5-7b-hf (7.1B, F16)
Alternatives at this size
Other models for image-text-to-text within about a third of llava-v1.6-mistral-7b-hf's 7.6B parameters, from other model lines.
- Qwen3.5-9B (9.7B, BF16)
- chandra-ocr-2 (5.3B, BF16)
- UI-TARS-1.5-7B (8.3B, F32)
- Cosmos-Reason2-8B (8.8B, BF16)
- Ovis2.5-9B (9.2B, BF16)
More on llava-v1.6-mistral-7b-hf
Fits on a 24 GB GPU at FP16: every model that fits in 24 GB.
Fits on a 12 GB GPU at FP8: every model that fits in 12 GB.
Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.
Best vision-language models: how llava-v1.6-mistral-7b-hf ranks against the rest.
Related reading: RTX A5000 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.
