What GPU do I need to run EleutherAI/pythia-1.4b-deduped?
1.4B parameters, published in F32. View on Hugging Face
Set up pythia-1.4b-dedupedpythia-1.4b-deduped is published by EleutherAI on Hugging Face, with 22,263 downloads and 22 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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 5.3 GB | 6.3 GB | P4 | 1 | $0.069/hr |
| FP8 (quantized) | 1.3 GB | 1.6 GB | RTX 5060 Ti | 1 | $0.160/hr |
| INT4 (quantized) | 0.7 GB | 0.8 GB | RTX 3060 | 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 pythia-1.4b-deduped at its published (F32) precision: 1× P4, at $0.069/hr per GPU ($0.069/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
pythia-1.4b-deduped: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Multi-head attention: every layer caches keys and values for every head. Cached per token: 196,608 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 2K (model maximum) | 0.38 GB |
Computed from the layer, head and window counts in the model's own configuration (EleutherAI/pythia-1.4b-deduped), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
pythia-1.4b-deduped: common questions
Does pythia-1.4b-deduped fit on a 8 GB GPU?
Yes. At FP32 it needs 6.3 GB of VRAM, so an 8 GB card holds it with 1.7 GB to spare. A 6 GB card is not enough for it at FP32.
Can pythia-1.4b-deduped run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 5.3 GB, or 6.3 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 2.6 GB, or 3.2 GB with overhead. That moves it onto a 6 GB card instead of an 8 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM pythia-1.4b-deduped can run in?
0.8 GB, at INT4 (quantized), which fits a 6 GB card, against 6.3 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.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Pythia models
- pythia-1.4b (1.5B, F16)
- pythia-1b (1.1B, F16)
- pythia-1b-deduped (1.1B, F16)
- pythia-2.8b (2.9B, F16)
- pythia-410m (506M, F16)
Alternatives at this size
Other models for text-generation within about a third of pythia-1.4b-deduped's 1.4B parameters, from other model lines.
- Qwen2.5-1.5B-Instruct (1.5B, BF16)
- Llama-3.2-1B-Instruct (1.2B, BF16)
- gemma-3-1b-it (1000M, BF16)
- OpenELM-1_1B-Instruct (1.1B, BF16)
- OLMo-2-0425-1B (1.5B, F32)
More on pythia-1.4b-deduped
Fits on an 8 GB GPU at FP32: every model that fits in 8 GB.
Best chat and assistants models: how pythia-1.4b-deduped ranks against the rest.
Related reading: H100 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
