How much VRAM does Qwen3-8B-DeepSeek-v3.2-Speciale-Distill need?

Q4_K_M is exactly 5,027,784,608 bytes (4.68 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is Qwen3-8B-DeepSeek-v3.2-Speciale-Distill'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.

TeichAI · text

Qwen3-8B-DeepSeek-v3.2-Speciale-Distill

TeichAI/Qwen3-8B-DeepSeek-v3.2-Speciale-Distill

Qwen3-8B-DeepSeek-v3.2-Speciale-Distill at Q4_K_M is exactly 5,027,784,608 bytes (4.68 GiB / 5.03 GB) — an effective 4.911 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

8.2B

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
IQ2_XS	2.51 GiB	2,696,156,704	2.633	—	TeichAI
IQ2_M	2.84 GiB	3,051,914,784	2.981	—	TeichAI
IQ3_XS	3.38 GiB	3,626,874,400	3.542	—	TeichAI
Q3_K_S	3.51 GiB	3,769,612,192	3.682	—	TeichAI
IQ3_M	3.63 GiB	3,896,620,576	3.806	—	TeichAI
Q3_K_M	3.84 GiB	4,124,161,952	4.028	—	TeichAI
IQ4_XS	4.25 GiB	4,561,839,648	4.456	—	TeichAI
IQ4_NL	4.49 GiB	4,818,790,304	4.707	—	TeichAI
Q4_K_M	4.68 GiB	5,027,784,608	4.911	—	TeichAI
F16	15.26 GiB	16,388,044,224	16.006	—	TeichAI
BF16	15.26 GiB	16,388,044,704	16.006	—	TeichAI
Q8_02 shards	16.22 GiB	17,419,038,048	17.013	—	TeichAI

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 gemma-4-E4B-it vs Qwen3-4B vs Qwen3-8B vs HyperCLOVAX-SEED-Text-Instruct-1.5B 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 Q4_K_M at roughly 4.29 GiB. The real file is 4.68 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

4096

Vocab

151,936

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

false

Questions people ask

How much VRAM does Qwen3-8B-DeepSeek-v3.2-Speciale-Distill need?: Q4_K_M is exactly 5,027,784,608 bytes (4.68 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Qwen3-8B-DeepSeek-v3.2-Speciale-Distill'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 Qwen3-8B-DeepSeek-v3.2-Speciale-Distill 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.