What GPU do I need to run intfloat/multilingual-e5-large-instruct?
560M parameters, published in F16. View on Hugging Face Full specs & deploy guide
Set up multilingua…5-large-instructmultilingual-e5-large-instruct is published by intfloat on Hugging Face, with 1,420,175 downloads and 641 likes to date. It's a XLMRobertaModel model built for feature-extraction, published natively in F16.
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) |
|---|---|---|---|---|---|
| FP16 | 1.0 GB | 1.3 GB | A16 | 1 | $0.059/hr |
| FP8 (quantized) | 0.5 GB | 0.6 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 0.3 GB | 0.3 GB | A16 | 1 | $0.059/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 multilingual-e5-large-instruct at its published (F16) precision: 1× A16, at $0.059/hr per GPU ($0.059/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
multilingual-e5-large-instruct: common questions
How much VRAM does multilingual-e5-large-instruct need?
1.3 GB at FP16, 0.6 GB at FP8 (quantized), 0.3 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 1.0 GB of weights plus inference overhead is the whole requirement.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
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