openbmb · vision language

MiniCPM-V-4_5

openbmb/MiniCPM-V-4_5

MiniCPM-V-4_5 at Q4_K_M is exactly 5,026,714,560 bytes (4.68 GiB / 5.03 GB) — an effective 4.624 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
8.7B
Architecture
qwen3
36 layers
Context
40,960
native (config.json)
License
—

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
Q2_K3.06 GiB3,280,843,9683.018—second-state
Q3_K_S3.51 GiB3,768,644,4163.467—second-state
Q3_K_M3.84 GiB4,123,194,1763.793—second-state
Q3_K_L4.13 GiB4,430,426,9444.076—second-state
Q4_04.45 GiB4,773,680,0644.392—second-state
Q4_K_S4.47 GiB4,800,942,7844.417—openbmb
Q4_K_S4.47 GiB4,800,943,0404.417—second-state
Q4_K_M4.68 GiB5,026,714,5604.624—second-state
Q4_K_M4.68 GiB5,026,714,6884.624—huihui-ai
Q4_14.89 GiB5,246,637,7604.827—openbmb
Q5_05.33 GiB5,719,595,7125.262—openbmb
Q5_05.33 GiB5,719,595,9685.262—second-state
Q5_K_S5.33 GiB5,719,595,9685.262—second-state
Q5_K_M5.45 GiB5,849,947,0725.382—second-state
Q6_K6.26 GiB6,724,631,6166.186—second-state
Q6_K6.26 GiB6,724,631,7446.186—huihui-ai
Q8_08.11 GiB8,707,877,6968.011—second-state
Q8_08.11 GiB8,707,877,8248.011—huihui-ai
Q4_02 shards8.89 GiB9,547,359,6168.783—openbmb
Q4_K_M2 shards9.36 GiB10,053,428,6089.249—openbmb
Q5_K_S2 shards10.65 GiB11,439,191,42410.524—openbmb
Q5_K_M2 shards10.90 GiB11,699,893,63210.764—openbmb
Q5_12 shards11.53 GiB12,385,107,32811.394—openbmb
Q6_K2 shards12.53 GiB13,449,262,72012.373—openbmb
F1615.26 GiB16,384,959,29615.074—second-state
F1615.26 GiB16,384,959,42415.074—huihui-ai
Q8_02 shards16.22 GiB17,415,754,88016.022—openbmb
F162 shards30.52 GiB32,769,918,08030.148—openbmb

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0960.56 GiB0.56 GiB—36 / 0 / 0
8,1921.13 GiB1.13 GiB—36 / 0 / 0
16,3842.25 GiB2.25 GiB—36 / 0 / 0
32,7684.50 GiB4.50 GiB—36 / 0 / 0
65,5369.00 GiB9.00 GiB—36 / 0 / 0
131,07218.00 GiB18.00 GiB—36 / 0 / 0

Compare with

same modality, comparable size
vs Qwen3.5-9Bvs gemma-4-12B-itvs Qwen3.5-4Bvs Qwythos-9B-Claude-Mythos-5-1Mvs Qwen3.5-0.8Bvs gemma-4-E2B-itAll alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 4.56 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
36
Attention heads
32
KV heads
8
Head dim
128
Hidden size
4096
Vocab
151,748
Sliding window
none
SWA period
—
MLA
no
Experts
—
Experts per token
—
use_sliding_window
false

Questions people ask

How much VRAM does MiniCPM-V-4_5 need?
Q4_K_M is exactly 5,026,714,560 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 MiniCPM-V-4_5'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 MiniCPM-V-4_5 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.