What GPU do I need to run stabilityai/stablelm-2-zephyr-1_6b?

1.6B parameters, published in F16. View on Hugging Face

1.6B
Parameters
F16
Native precision
StableLmForCausalLM
Architecture
text-generation
Pipeline

stablelm-2-zephyr-1_6b is published by stabilityai on Hugging Face, with 11,271 downloads and 187 likes to date. It's a StableLmForCausalLM model built for text-generation, published natively in F16.

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.

PrecisionWeight sizeRequired VRAMCheapest live fitGPUs neededEst. $/hr (full fit)
FP163.1 GB3.7 GBV1001$0.088/hr
FP8 (quantized)1.5 GB1.8 GBRTX 5060 Ti1$0.110/hr
INT4 (quantized)0.8 GB0.9 GBRTX 5060 Ti1$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 stablelm-2-zephyr-1_6b at its published (F16) precision: 1× V100, 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.

stablelm-2-zephyr-1_6b: common questions

How much VRAM does stablelm-2-zephyr-1_6b need?

3.7 GB at FP16, 1.8 GB at FP8 (quantized), 0.9 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 3.1 GB of weights plus inference overhead is the whole requirement.

How many copies of stablelm-2-zephyr-1_6b fit on one V100?

4, by VRAM alone. That card carries 16.0 GB and one copy needs 3.7 GB at FP16, on a live rate of $0.088/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.

More StableLM models

All 3 StableLM models: VRAM and GPU requirements

Related reading: V100 pricing and specs, and The best GPUs for AI, ranked.

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