What GPU do I need to run speakleash/Bielik-11B-v3.0-Instruct?
11.2B parameters, published in BF16. View on Hugging FaceGated
Set up Bielik-11B-v3.0-InstructBielik-11B-v3.0-Instruct is published by speakleash on Hugging Face, with 66,481 downloads and 87 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16, and gated: you'll need to accept the model's terms on Hugging Face before downloading weights.
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.
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 Bielik-11B-v3.0-Instruct at its published (BF16) precision: 1× RTX A6000 on runpod, at $0.330/hr per GPU ($0.330/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Bielik-11B-v3.0-Instruct: common questions
Does Bielik-11B-v3.0-Instruct fit on a 32 GB GPU?
Yes. At BF16 it needs 25.0 GB of VRAM, so a 32 GB card holds it with 7.0 GB to spare. A 24 GB card is not enough for it at BF16.
Do I need approval to download Bielik-11B-v3.0-Instruct?
Yes. speakleash gates this repository on Hugging Face, so you have to accept its terms with a signed-in account before the weights will download. It does not change the 25.0 GB the model needs once you have them.
What is the least VRAM Bielik-11B-v3.0-Instruct can run in?
6.2 GB, at INT4 (quantized), which fits an 8 GB card, against 25.0 GB at BF16. 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 Bielik-11B-v3.0-Instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A6000 on runpod at $0.330/hr. At INT4 (quantized) it drops to one RTX 3070 on simplepod at $0.050/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
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