What GPU do I need to run LSX-UniWue/LLaMmlein_1B_prerelease?
1.1B parameters, published in F32. View on Hugging Face
Set up LLaMmlein_1B_prereleaseLLaMmlein_1B_prerelease is published by LSX-UniWue on Hugging Face, with 183,437 downloads and 14 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 | 4.1 GB | 4.9 GB | RTX 3070 | 1 | $0.088/hr |
| FP8 (quantized) | 1.0 GB | 1.2 GB | RTX 4070 | 1 | $0.121/hr |
| INT4 (quantized) | 0.5 GB | 0.6 GB | RTX 3070 | 1 | $0.088/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 LLaMmlein_1B_prerelease at its published (F32) precision: 1× RTX 3070, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
LLaMmlein_1B_prerelease: common questions
How much VRAM does LLaMmlein_1B_prerelease need?
4.9 GB at FP32, 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 4.1 GB of weights plus inference overhead is the whole requirement.
Can LLaMmlein_1B_prerelease run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 4.1 GB, or 4.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 2.0 GB, or 2.5 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More LSX-UniWue models
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
- Qwen3.5-9B (9.7B, BF16)
Related reading: RTX 3070 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.
