What GPU do I need to run llava-hf/llava-1.5-13b-hf?
13.4B parameters, published in F16. View on Hugging Face
Set up llava-1.5-13b-hfllava-1.5-13b-hf is published by llava-hf on Hugging Face, with 151,909 downloads and 35 likes to date. It's a LlavaForConditionalGeneration 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 | 24.9 GB | 29.8 GB | V100 | 1 | $0.187/hr |
| FP8 (quantized) | 12.4 GB | 14.9 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 6.2 GB | 7.5 GB | RTX 3060 | 1 | $0.110/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-1.5-13b-hf at its published (F16) precision: 1× V100, at $0.187/hr per GPU ($0.187/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
llava-1.5-13b-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.
Multi-head attention: every layer caches keys and values for every head. Cached per token: 819,200 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 3.1 GB |
Computed from the layer, head and window counts in the model's own configuration (llava-hf/llava-1.5-13b-hf), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
llava-1.5-13b-hf: common questions
Does llava-1.5-13b-hf fit on a 32 GB GPU?
Yes. At FP16 it needs 29.8 GB of VRAM, so a 32 GB card holds it with 2.2 GB to spare. A 24 GB card is not enough for it at FP16.
What is the least VRAM llava-1.5-13b-hf can run in?
7.5 GB, at INT4 (quantized), which fits an 8 GB card, against 29.8 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-1.5-13b-hf lower the GPU bill?
Yes. At FP16 the cheapest live fit is one V100 at $0.187/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/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-1.5-7b-hf (7.1B, F16)
- llava-onevision-qwen2-7b-ov (8.0B, BF16)
- LLaVA-Video-7B-Qwen2 (8.0B, BF16)
- llava-v1.6-mistral-7b-hf (7.6B, F16)
- llava-v1.6-mistral-7b (7.6B, BF16)
Alternatives at this size
Other models for image-text-to-text within about a third of llava-1.5-13b-hf's 13.4B parameters, from other model lines.
- Qwen3.5-9B (9.7B, BF16)
- gemma-3-12b-it (12.2B, BF16)
- Ovis2.5-9B (9.2B, BF16)
- Kimi-VL-A3B-Instruct (16.4B, BF16)
- GLM-4.1V-9B-Thinking (10.3B, BF16)
More on llava-1.5-13b-hf
Fits on a 32 GB GPU at FP16: every model that fits in 32 GB.
Fits on a 16 GB GPU at FP8: every model that fits in 16 GB.
Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.
Best vision-language models: how llava-1.5-13b-hf ranks against the rest.
Related reading: V100 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.
