How much VRAM does T-lite-it-2.1 need?

Q4_K_M is exactly 5,027,750,240 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 T-lite-it-2.1'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.

t-tech · text

T-lite-it-2.1

t-tech/T-lite-it-2.1

T-lite-it-2.1 at Q4_K_M is exactly 5,027,750,240 bytes (4.68 GiB / 5.03 GB) — an effective 4.912 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

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q4_K_M	4.68 GiB	5,027,750,240	4.912	—	t-tech
Q5_0	5.33 GiB	5,720,601,440	5.589	—	t-tech
Q5_K_S	5.33 GiB	5,720,601,440	5.589	—	t-tech
Q5_K_M	5.45 GiB	5,850,952,512	5.716	—	AGmind
Q5_K_M	5.45 GiB	5,850,952,544	5.716	—	t-tech
Q6_K	6.26 GiB	6,725,604,992	6.571	—	t-tech
Q8_0	8.11 GiB	8,708,734,016	8.508	—	t-tech

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-Coder-30B-A3B-Instruct vs Qwen3.6-27B vs Qwen3.8-27B vs gemma-4-12B-it-qat-q4_0-unquantized vs gemma-4-E4B-it vs Qwen3-30B-A3B-Thinking-2507 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

151,689

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

false

Questions people ask

How much VRAM does T-lite-it-2.1 need?: Q4_K_M is exactly 5,027,750,240 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 T-lite-it-2.1'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 T-lite-it-2.1 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.