Coding · deepseek-ai
DeepSeek-Coder-V2-Lite-Instruct
DeepSeek Coder V2 Lite, instruct weights. Lite is the publisher's name. The gigabytes still come from the parameter count on the record.
Freshness uses the repository open date. This record is 870 days old, opened 2024-06-14. Popularity uses 214,347 downloads and 710 likes listed by Hugging Face. Checked 2026-11-01. Neither number grades answers. Formulas.
The parameter count is the total on the record. A mixture-of-experts model does not use every parameter on every token. The file you download is still the full count.
Hugging Face reports 31.4 GB of storage for the whole repository. That figure can include several packs, so the fit table does not use it as the size of one file.
Will it fit?
Save a machine on the hardware page and this block fills in. The table already has the sizes.
| Pack | Size | Saved machine |
|---|---|---|
| Weight shards summed | 31.4 GB weights + 1.5 GB allowance. File: 4 shard files in the repository root. | Choose a machine |
| Q4 estimate | 8.6 GB weights + 1.5 GB allowance. | Choose a machine |
| Q8 estimate | 16.5 GB weights + 1.5 GB allowance. | Choose a machine |
| FP16 estimate | 31.4 GB weights + 1.5 GB allowance. | Choose a machine |
Estimate rows use the parameter count. A measured row names a file in the repository. The allowance is fixed: it stands in for context or activations, and it is not a measurement from a run.
How to run it
Text models are usually loaded from a quantized pack in llama.cpp, Ollama, or LM Studio, or from the original weights with the publisher's own instructions. AI Price Desk does not host the weights.
Publisher repository on Hugging Face
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This directory is assembled from public model records. Freshness is age. Popularity is downloads and likes. A fit figure is an estimate from a parameter count or from a published file size, not a run on our hardware. How the numbers are made. AI disclosure.