What GPU do I need to run Salesforce/blip2-opt-2.7b?
3.7B parameters, published in F32. View on Hugging Face
Set up blip2-opt-2.7bblip2-opt-2.7b is published by Salesforce on Hugging Face, with 462,510 downloads and 447 likes to date. It's a Blip2ForConditionalGeneration model built for image-text-to-text, published natively in F32.
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) |
|---|---|---|---|---|---|
| FP32 | 14.0 GB | 16.7 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 3.5 GB | 4.2 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 1.7 GB | 2.1 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 blip2-opt-2.7b at its published (F32) 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.
blip2-opt-2.7b: 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: 327,680 bytes at 16-bit.
The model's maximum context length is not published in its configuration, so no per-context figures are shown.
Computed from the layer, head and window counts in the model's own configuration (Salesforce/blip2-opt-2.7b), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
blip2-opt-2.7b: common questions
Does blip2-opt-2.7b fit on a 24 GB GPU?
Yes. At FP32 it needs 16.7 GB of VRAM, so a 24 GB card holds it with 7.3 GB to spare. A 16 GB card is not enough for it at FP32.
Can blip2-opt-2.7b run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 14.0 GB, or 16.7 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 7.0 GB, or 8.4 GB with overhead. That moves it onto a 12 GB card instead of a 24 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM blip2-opt-2.7b can run in?
2.1 GB, at INT4 (quantized), which fits a 6 GB card, against 16.7 GB at FP32. 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 blip2-opt-2.7b lower the GPU bill?
Yes. At FP32 the cheapest live fit is one RTX A5000 at $0.176/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 BLIP models
- blip2-flan-t5-xl (3.9B, F32)
- blip-image-captioning-large (470M, F32)
Alternatives at this size
Other models for image-text-to-text within about a third of blip2-opt-2.7b's 3.7B parameters, from other model lines.
- Qwen3.5-4B (4.7B, BF16)
- Unlimited-OCR (3.3B, BF16)
- chandra-ocr-2 (5.3B, BF16)
- DeepSeek-OCR (3.3B, BF16)
- gemma-3-4b-it (4.3B, BF16)
More on blip2-opt-2.7b
Fits on a 24 GB GPU at FP32: every model that fits in 24 GB.
Fits on an 8 GB GPU at FP8: every model that fits in 8 GB.
Best vision-language models: how blip2-opt-2.7b 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.
