Embedding Parameter & Memory Calculator
Vocab × hidden embedding-table cost — tied vs untied, plus the memory bill at FP16/INT8.
Defaults are Gemma 2 9B: its 256K vocab embedding is ~0.92B parameters (tied) — 10% of the whole model, the largest embedding share of any mainstream LLM.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Embedding Parameter & Memory Calculator
Vocabulary size is a hidden architecture tax: every extra token costs H parameters in the input table and, if untied, H more in the output head. This calculator prices that decision for any vocab/hidden combination, in parameters and gigabytes. Big vocabs (Gemma's 256K, Qwen's 152K) tokenize multilingual text into fewer tokens — faster, cheaper inference per sentence — but small models pay a disproportionate share of their budget for the table. That is exactly why small models tie embeddings and large ones often do not.
How to use Embedding Parameter & Memory Calculator
- 1Enter your values into Embedding Parameter & Memory Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Embedding Parameter & Memory Calculator?
- ✓Computes Embedding Parameter & Memory instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: params = (tied ? 1 : 2) × V × H.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why do big vocabularies help non-English text?+
More vocabulary slots mean whole words and morphemes in Hindi, Chinese or code get single tokens instead of byte fragments. The same sentence becomes 20–40% fewer tokens, which cuts latency, KV-cache use and API cost proportionally.
When should embeddings be tied?+
When the table is a large fraction of the model. A 1B model with a 100K vocab and H=2048 would spend 0.2B×2 = 40% of its budget untied — tying halves that for near-zero quality cost at small scale. 70B-class models untie because 0.5% extra is negligible.
Does the embedding table affect inference speed?+
The input lookup is trivial, but the output head is a full V×H matmul every generated token. At V=256K, H=3584 that is ~1.8 GFLOPs per token — for small models a significant slice of decode compute, another argument for modest vocabs on edge models.
Can I shrink the embedding of an existing model?+
Yes — vocabulary pruning removes rows for tokens your domain never uses, and the table can be quantized to INT8 separately (it is robust to it). Both are common tricks for fitting 1–3B models into phone-class memory budgets.
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