How much VRAM does rp-model_E2B_v5.1 need?

I1-Q2_K is exactly 2,989,081,824 bytes (2.78 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is rp-model_E2B_v5.1's KV cache?

0.25 GiB at 32K context with an f16 cache, computed per layer from the model's architecture — most of its layers use sliding-window attention, so a flat formula overstates this substantially. Quantizing the cache to q8_0 roughly halves it.

3fou34jfioqjf3q0pf · text

rp-model_E2B_v5.1

3fou34jfioqjf3q0pf/rp-model_E2B_v5.1

rp-model_E2B_v5.1 at I1-Q2_K is exactly 2,989,081,824 bytes (2.78 GiB / 2.99 GB) — an effective 4.668 bits per weight, not the nominal 1. Its KV cache at 32K is 0.25 GiB, not the 1.09 GiB a flat formula predicts.

From the file· summed from 1 file(s)From the file· KV per layer

Parameters

5.1B

Architecture

gemma4

35 layers

Context

131,072

native (config.json)

License

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
I1-Q2_K	2.78 GiB	2,989,081,824	4.668	—	mradermacher
I1-Q3_K_S	2.90 GiB	3,110,210,784	4.857	—	mradermacher
I1-IQ3_S	2.90 GiB	3,112,404,192	4.860	—	mradermacher
I1-IQ3_M	2.92 GiB	3,134,964,960	4.895	—	mradermacher
I1-Q3_K_M	2.98 GiB	3,201,344,736	4.999	—	mradermacher
I1-Q3_K_L	3.06 GiB	3,282,347,232	5.125	—	mradermacher
I1-IQ4_XS	3.08 GiB	3,303,924,960	5.159	—	mradermacher
I1-IQ4_NL	3.13 GiB	3,362,219,232	5.250	—	mradermacher
I1-Q4_0	3.13 GiB	3,362,514,144	5.251	—	mradermacher
I1-Q4_K_S	3.13 GiB	3,365,070,048	5.255	—	mradermacher
I1-Q4_K_M	3.19 GiB	3,427,874,016	5.353	—	mradermacher
I1-Q4_1	3.24 GiB	3,477,775,584	5.431	—	mradermacher
I1-Q5_K_S	3.35 GiB	3,595,396,320	5.614	—	mradermacher
I1-Q5_K_M	3.38 GiB	3,630,281,952	5.669	—	mradermacher
I1-Q6_K	3.58 GiB	3,845,340,384	6.005	—	mradermacher

KV cache by context

computed per layer — this model uses sliding-window attention

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.05 GiB	0.14 GiB	2.50×	7 / 28 / 0
8,192	0.08 GiB	0.27 GiB	3.33×	7 / 28 / 0
16,384	0.14 GiB	0.55 GiB	4.00×	7 / 28 / 0
32,768	0.25 GiB	1.09 GiB	4.44×	7 / 28 / 0
65,536	0.46 GiB	2.19 GiB	4.71×	7 / 28 / 0
131,072	0.90 GiB	4.38 GiB	4.85×	7 / 28 / 0

28 of 35 layers cache only a 512-token window rather than the full context, on a period of . Figures assume the default configuration; --swa-full disables the saving entirely.

Compare with

same modality, comparable size

vs gemma-4-12B-it-qat-q4_0-unquantized vs gemma-4-E4B-it vs Qwen3-4B vs Qwen3-8B vs Llama-3.2-1B-Instruct vs llama-3-youko-8b All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts I1-Q2_K at roughly 2.68 GiB. The real file is 2.78 GiB, because a quantization is a mixture and some tensors are always kept at higher precision. The larger discrepancy is the cache: a flat formula gives 1.09 GiB at 32K context where the real figure is 0.25 GiB, because most of this model's layers cache a fixed window rather than the whole context.

Architecture

from config.json

Layers

Attention heads

KV heads

Head dim

256

Hidden size

1536

Vocab

262,144

Sliding window

512

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does rp-model_E2B_v5.1 need?: I1-Q2_K is exactly 2,989,081,824 bytes (2.78 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is rp-model_E2B_v5.1's KV cache?: 0.25 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of rp-model_E2B_v5.1 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.