What GPU do I need to run muse-bench/MUSE-news_target?
6.7B parameters, published in F32. View on Hugging Face
Set up MUSE-news_targetMUSE-news_target is published by muse-bench on Hugging Face, with 14,186 downloads and 3 likes to date. It's a LlamaForCausalLM 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 | 25.1 GB | 30.1 GB | V100 | 1 | $0.187/hr |
| cheaper alt. | V100 | 2 | $0.176/hr | ||
| FP8 (quantized) | 6.3 GB | 7.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.1 GB | 3.8 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 MUSE-news_target at its published (F32) 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.
MUSE-news_target: common questions
Does MUSE-news_target fit on a 32 GB GPU?
Yes. At FP32 it needs 30.1 GB of VRAM, so a 32 GB card holds it with 1.9 GB to spare. A 24 GB card is not enough for it at FP32.
Can MUSE-news_target run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 25.1 GB, or 30.1 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 12.6 GB, or 15.1 GB with overhead. That moves it onto a 16 GB card instead of a 32 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM MUSE-news_target can run in?
3.8 GB, at INT4 (quantized), which fits a 6 GB card, against 30.1 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 MUSE-news_target lower the GPU bill?
Yes. At FP32 the cheapest live fit is one V100 at $0.187/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: V100 pricing and specs, and The best GPUs for AI, ranked.
