How much VRAM does lift need?

Q4_K_M is exactly 5,780,090,336 bytes (5.38 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is lift's KV cache?

1.00 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.

datalab-to · vision language

lift

datalab-to/lift

lift at Q4_K_M is exactly 5,780,090,336 bytes (5.38 GiB / 5.78 GB) — an effective 4.790 bits per weight, not the nominal 4. Its KV cache at 32K is 1.00 GiB.

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

Parameters

9.7B

Architecture

qwen35

32 layers

Context

262,144

native (config.json)

License

openrail

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q2_K	3.65 GiB	3,914,968,544	3.244	—	prithivMLmods
Q3_K_S	4.06 GiB	4,364,021,216	3.617	—	prithivMLmods
IQ3_M	4.21 GiB	4,522,782,176	3.748	—	SandLogicTechnologies
Q3_K_M	4.41 GiB	4,737,609,184	3.926	—	prithivMLmods
Q3_K_L	4.70 GiB	5,048,511,968	4.184	—	prithivMLmods
IQ4_XS	4.99 GiB	5,357,874,656	4.440	—	SandLogicTechnologies
Q4_0	5.08 GiB	5,450,280,416	4.517	—	prithivMLmods
Q4_K_S	5.11 GiB	5,488,553,440	4.549	—	prithivMLmods
IQ4_NL	5.20 GiB	5,580,828,128	4.625	—	SandLogicTechnologies
Q4_K_M	5.38 GiB	5,780,090,336	4.790	—	prithivMLmods
Q5_0	6.03 GiB	6,472,642,016	5.364	—	prithivMLmods
Q5_K_S	6.03 GiB	6,472,642,016	5.364	—	prithivMLmods
Q5_K_M	6.19 GiB	6,642,544,096	5.505	—	prithivMLmods
Q6_K	7.04 GiB	7,558,901,216	6.264	—	prithivMLmods
Q8_0	9.11 GiB	9,786,060,256	8.110	—	prithivMLmods
BF16	17.14 GiB	18,407,321,056	15.255	—	prithivMLmods
F16	17.14 GiB	18,407,321,056	15.255	—	prithivMLmods

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.13 GiB	0.50 GiB	4.00×	8 / 0 / 24
8,192	0.25 GiB	1.00 GiB	4.00×	8 / 0 / 24
16,384	0.50 GiB	2.00 GiB	4.00×	8 / 0 / 24
32,768	1.00 GiB	4.00 GiB	4.00×	8 / 0 / 24
65,536	2.00 GiB	8.00 GiB	4.00×	8 / 0 / 24
131,072	4.00 GiB	16.00 GiB	4.00×	8 / 0 / 24

24 of 32 layers use linear attention, which keeps a fixed-size recurrent state instead of a per-token cache. Those layers do not grow with context at all — treating them as ordinary attention, as a flat formula does, overstates this model's cache by roughly 4.0× at long context.

Compare with

same modality, comparable size

vs Qwen3.6-35B-A3B vs Qwen3.5-9B vs gemma-4-26B-A4B-it vs gemma-4-12B-it vs Qwen3.5-4B vs Qwythos-9B-Claude-Mythos-5-1M 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 5.06 GiB. The real file is 5.38 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

256

Hidden size

4096

Vocab

248,320

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does lift need?: Q4_K_M is exactly 5,780,090,336 bytes (5.38 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is lift's KV cache?: 1.00 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 lift 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.