What GPU do I need to run realtreetune/rho-1b-sft-MATH?
1.1B parameters, published in BF16. View on Hugging Face
Set up rho-1b-sft-MATHrho-1b-sft-MATH is published by realtreetune on Hugging Face, with 17,025 downloads and 0 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
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) |
|---|---|---|---|---|---|
| BF16 | 2.0 GB | 2.5 GB | RTX 4070 Super | 1 | $0.121/hr |
| FP8 (quantized) | 1.0 GB | 1.2 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 0.5 GB | 0.6 GB | RTX 4070 Super | 1 | $0.121/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 rho-1b-sft-MATH at its published (BF16) precision: 1× RTX 4070 Super, at $0.121/hr per GPU ($0.121/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
rho-1b-sft-MATH: common questions
How much VRAM does rho-1b-sft-MATH need?
2.5 GB at BF16, 1.2 GB at FP8 (quantized), 0.6 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 2.0 GB of weights plus inference overhead is the whole requirement.
How many copies of rho-1b-sft-MATH fit on one RTX 4070 Super?
4, by VRAM alone. That card carries 12.0 GB and one copy needs 2.5 GB at BF16, on a live rate of $0.121/hr for the whole card. Throughput is not modelled here, so 4 copies is not 4 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
