How much VRAM does Qwen2.5-1.5B-Instruct-uncensored need?

Q4_K_M is exactly 1,117,321,536 bytes (1.04 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Qwen2.5-1.5B-Instruct-uncensored's KV cache?

0.88 GiB at 32K context with an f16 cache, computed per layer from the model's architecture. Quantizing the cache to q8_0 roughly halves it.

thirdeyeai · text

Qwen2.5-1.5B-Instruct-uncensored

thirdeyeai/Qwen2.5-1.5B-Instruct-uncensored

Qwen2.5-1.5B-Instruct-uncensored at Q4_K_M is exactly 1,117,321,536 bytes (1.04 GiB / 1.12 GB) — an effective 5.030 bits per weight, not the nominal 4. Its KV cache at 32K is 0.88 GiB.

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

Parameters

1.8B

Architecture

qwen2

28 layers

Context

32,768

native (config.json)

License

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q2_K	0.70 GiB	752,880,960	3.389	—	mradermacher
Q3_K_S	0.80 GiB	861,222,720	3.877	—	mradermacher
Q3_K_M	0.86 GiB	924,456,768	4.162	—	mradermacher
Q3_K_L	0.91 GiB	980,440,896	4.414	—	mradermacher
IQ4_XS	0.96 GiB	1,026,163,008	4.620	—	mradermacher
Q4_K_S	1.00 GiB	1,071,585,600	4.824	—	mradermacher
Q4_K_M	1.04 GiB	1,117,321,536	5.030	—	mradermacher
Q5_K_S	1.17 GiB	1,259,174,208	5.668	—	mradermacher
Q5_K_M	1.20 GiB	1,285,495,104	5.787	—	mradermacher
Q6_K	1.36 GiB	1,464,179,520	6.591	—	mradermacher
Q8_0	1.76 GiB	1,894,532,928	8.529	—	mradermacher
F16	3.32 GiB	3,560,417,088	16.028	—	mradermacher

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.11 GiB	0.11 GiB	—	28 / 0 / 0
8,192	0.22 GiB	0.22 GiB	—	28 / 0 / 0
16,384	0.44 GiB	0.44 GiB	—	28 / 0 / 0
32,768	0.88 GiB	0.88 GiB	—	28 / 0 / 0
65,536	1.75 GiB	1.75 GiB	—	28 / 0 / 0
131,072	3.50 GiB	3.50 GiB	—	28 / 0 / 0

Compare with

same modality, comparable size

vs Qwen3-4B vs HyperCLOVAX-SEED-Text-Instruct-1.5B vs Llama-3.2-1B-Instruct vs ced-base vs gemma-3-1b-it vs Qwen3-1.7B All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 0.93 GiB. The real file is 1.04 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

128

Hidden size

1536

Vocab

151,936

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

false

Questions people ask

How much VRAM does Qwen2.5-1.5B-Instruct-uncensored need?: Q4_K_M is exactly 1,117,321,536 bytes (1.04 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Qwen2.5-1.5B-Instruct-uncensored's KV cache?: 0.88 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 Qwen2.5-1.5B-Instruct-uncensored 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.