How much VRAM does Qwen3-4B need?

Q4_K_M is exactly 2,497,280,256 bytes (2.33 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Qwen3-4B's KV cache?

4.50 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.

Qwen · text

Qwen3-4B

Qwen/Qwen3-4B

Qwen3-4B at Q4_K_M is exactly 2,497,280,256 bytes (2.33 GiB / 2.50 GB) — an effective 4.967 bits per weight, not the nominal 4. Its KV cache at 32K is 4.50 GiB.

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

Parameters

4.0B

Architecture

qwen3

36 layers

Context

40,960

native (config.json)

License

—

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
UD-IQ1_S	1.01 GiB	1,083,150,112	2.154	—	unsloth
UD-IQ1_M	1.06 GiB	1,143,484,192	2.274	—	unsloth
UD-IQ2_XXS	1.17 GiB	1,255,345,952	2.497	—	unsloth
UD-IQ2_M	1.43 GiB	1,532,931,872	3.049	—	unsloth
Q2_K	1.55 GiB	1,669,500,192	3.320	—	unsloth
Q2_K_L	1.55 GiB	1,669,500,192	3.320	—	unsloth
UD-IQ3_XXS	1.56 GiB	1,674,376,992	3.330	—	unsloth
Q3_K_S	1.76 GiB	1,886,997,792	3.753	—	unsloth
Q3_K_M	1.93 GiB	2,075,618,592	4.128	398	unsloth
Q3_K_L	2.09 GiB	2,239,785,664	4.455	—	lmstudio-community
IQ4_XS	2.11 GiB	2,270,752,032	4.516	398	unsloth
Q4_0	2.21 GiB	2,375,773,472	4.725	398	unsloth
IQ4_NL	2.22 GiB	2,381,344,032	4.736	—	unsloth
Q4_K_S	2.22 GiB	2,383,310,112	4.740	—	unsloth
Q4_K_M	2.33 GiB	2,497,280,256	4.967	398	Qwen
Q4_K_M	2.33 GiB	2,497,280,704	4.967	398	lmstudio-community
Q4_K_M	2.33 GiB	2,497,281,312	4.967	—	unsloth
Q4_1	2.42 GiB	2,596,629,792	5.164	—	unsloth
Q5_0	2.63 GiB	2,823,710,976	5.616	—	Qwen
Q5_K_S	2.63 GiB	2,823,712,032	5.616	—	unsloth
Q5_K_M	2.69 GiB	2,889,513,184	5.747	398	Qwen
Q5_K_M	2.69 GiB	2,889,514,272	5.747	—	unsloth
Q6_K	3.08 GiB	3,306,260,704	6.576	398	Qwen
Q6_K	3.08 GiB	3,306,261,184	6.576	398	lmstudio-community
Q6_K	3.08 GiB	3,306,261,792	6.576	—	unsloth
Q8_0	3.99 GiB	4,280,404,704	8.513	398	Qwen
Q8_0	3.99 GiB	4,280,405,184	8.513	398	lmstudio-community
Q8_0	3.99 GiB	4,280,405,792	8.513	—	unsloth
BF16	7.50 GiB	8,051,285,536	16.013	—	unsloth

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.56 GiB	0.56 GiB	—	36 / 0 / 0
8,192	1.13 GiB	1.13 GiB	—	36 / 0 / 0
16,384	2.25 GiB	2.25 GiB	—	36 / 0 / 0
32,768	4.50 GiB	4.50 GiB	—	36 / 0 / 0
65,536	9.00 GiB	9.00 GiB	—	36 / 0 / 0
131,072	18.00 GiB	18.00 GiB	—	36 / 0 / 0

Compare with

same modality, comparable size

vs Qwen3-8B vs HyperCLOVAX-SEED-Text-Instruct-1.5B vs Llama-3.2-1B-Instruct vs Qwen3-VL-8B-Instruct-abliterated-v1 vs Llama-3.1-8B-Instruct vs gemma-3-1b-it 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 2.11 GiB. The real file is 2.33 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

2560

Vocab

151,936

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

false

Questions people ask

How much VRAM does Qwen3-4B need?: Q4_K_M is exactly 2,497,280,256 bytes (2.33 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Qwen3-4B's KV cache?: 4.50 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 Qwen3-4B 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.