What GPU do I need to run tomg-group-umd/huginn-0125?
3.9B parameters, published in F32. View on Hugging Face
Set up huginn-0125huginn-0125 is published by tomg-group-umd on Hugging Face, with 18,685 downloads and 304 likes to date. It's a RavenForCausalLM model built for text-generation, published natively in F32.
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) |
|---|---|---|---|---|---|
| FP32 | 14.6 GB | 17.5 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 3.6 GB | 4.4 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 1.8 GB | 2.2 GB | RTX 5060 Ti | 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 huginn-0125 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.
huginn-0125: common questions
Does huginn-0125 fit on a 24 GB GPU?
Yes. At FP32 it needs 17.5 GB of VRAM, so a 24 GB card holds it with 6.5 GB to spare. A 16 GB card is not enough for it at FP32.
Can huginn-0125 run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 14.6 GB, or 17.5 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 7.3 GB, or 8.7 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 huginn-0125 can run in?
2.2 GB, at INT4 (quantized), which fits a 6 GB card, against 17.5 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 huginn-0125 lower the GPU bill?
Yes. At FP32 the cheapest live fit is one RTX A5000 at $0.176/hr. At FP8 (quantized) it drops to one RTX 5060 Ti 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.
Related reading: RTX A5000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
