Qwen · text

Qwen3-8B-Base

Qwen/Qwen3-8B-Base

Qwen3-8B-Base at Q4_K_M is exactly 4,676,804,928 bytes (4.36 GiB / 4.68 GB) — an effective 4.568 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
32,768
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
Q4_K_M4.36 GiB4,676,804,9284.568Qwen
Q4_04.45 GiB4,774,749,0244.664Antigma
Q4_K_S4.47 GiB4,802,012,0004.690Antigma
Q4_K_M4.68 GiB5,027,783,5204.911Antigma
Q4_K_M4.68 GiB5,027,784,0004.911Pomoika24
Q5_04.93 GiB5,291,991,3605.169Qwen
Q5_K_M5.05 GiB5,422,342,4645.296Qwen
Q5_K_M5.45 GiB5,851,112,7685.715Pomoika24
Q6_K5.79 GiB6,214,476,0966.070Qwen
Q8_07.49 GiB8,047,105,8247.860Qwen
F1614.10 GiB15,141,156,38414.789Qwen

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.56 GiB0.56 GiB36 / 0 / 0
8,1921.13 GiB1.13 GiB36 / 0 / 0
16,3842.25 GiB2.25 GiB36 / 0 / 0
32,7684.50 GiB4.50 GiB36 / 0 / 0
65,5369.00 GiB9.00 GiB36 / 0 / 0
131,07218.00 GiB18.00 GiB36 / 0 / 0

Compare with

same modality, comparable size

Will it run on your card?

full quant x context sweep

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 4.36 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
36
Attention heads
32
KV heads
8
Head dim
128
Hidden size
4096
Vocab
151,936
Sliding window
none
SWA period
MLA
no
Experts
Experts per token
use_sliding_window
false

Questions people ask

How much VRAM does Qwen3-8B-Base need?
Q4_K_M is exactly 4,676,804,928 bytes (4.36 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-Base'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-Base 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.