How much VRAM does gliner2-base-v1 need?

Q4_K is exactly 134,758,528 bytes (0.13 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

fastino · text

gliner2-base-v1

fastino/gliner2-base-v1

gliner2-base-v1 at Q4_K is exactly 134,758,528 bytes (0.13 GiB / 0.13 GB) — an effective 5.171 bits per weight, not the nominal 4.

From the file· summed from 2 file(s)From the file· KV per layer

Parameters

208M

Architecture

deberta

null layers

Context

—

native (config.json)

License

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q4_K2 shards	0.13 GiB	134,758,528	5.171	—	antflydb

Compare with

same modality, comparable size

vs ced-base vs embeddinggemma-300m vs Qwen3-0.6B vs Qwen2.5-0.5B-Instruct vs SmolLM2-135M-Instruct vs gemma-3-270m-it All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K at roughly 0.11 GiB. The real file is 0.13 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json

Layers

—

Attention heads

—

KV heads

—

Head dim

—

Hidden size

—

Vocab

—

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does gliner2-base-v1 need?: Q4_K is exactly 134,758,528 bytes (0.13 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of gliner2-base-v1 should I use?: Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.