What GPU do I need to run mnoukhov/pythia410m-sft-tldr?

405M parameters, published in F32. View on Hugging Face

405M
Parameters
F32
Native precision
GPTNeoXForCausalLM
Architecture
text-generation
Pipeline

pythia410m-sft-tldr is published by mnoukhov on Hugging Face, with 12,858 downloads and 0 likes to date. It's a GPTNeoXForCausalLM 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
1.5 GB
1.8 GB
P4 (akash)
1
$0.032/hr
FP8 (quantized)
0.4 GB
0.5 GB
RTX 4070 (simplepod)
1
$0.080/hr
INT4 (quantized)
0.2 GB
0.2 GB
RTX 3070 (simplepod)
1
$0.050/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 caveat: requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.

Cheapest way to run pythia410m-sft-tldr at its published (F32) precision: 1× P4 on akash, at $0.032/hr per GPU ($0.032/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.

Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.

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