beyoru · text · mixture of experts

Luna-7B-A4B

beyoru/Luna-7B-A4B

Luna-7B-A4B at Q4_K_M is exactly 4,126,489,312 bytes (3.84 GiB / 4.13 GB) — an effective 4.918 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
6.7B
total, not active
Architecture
qwen3moe
36 layers
Context
262,144
native (config.json)
License
mit

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S1.49 GiB1,594,539,4561.901mradermacher
I1-IQ1_M1.61 GiB1,727,004,0962.058mradermacher
I1-IQ2_XXS1.81 GiB1,947,778,4962.321mradermacher
I1-IQ2_XS1.99 GiB2,136,194,4962.546mradermacher
I1-IQ2_S2.06 GiB2,209,512,8962.633mradermacher
I1-IQ2_M2.22 GiB2,386,132,4162.844mradermacher
I1-Q2_K_S2.30 GiB2,470,067,1362.944mradermacher
Q2_K2.46 GiB2,643,819,2323.151mradermacher
I1-Q2_K2.46 GiB2,643,819,4563.151mradermacher
I1-IQ3_XXS2.52 GiB2,700,541,3763.219mradermacher
I1-IQ3_XS2.71 GiB2,907,765,6963.466mradermacher
Q3_K_S2.83 GiB3,043,424,9923.627mradermacher
I1-Q3_K_S2.83 GiB3,043,425,2163.627mradermacher
I1-IQ3_S2.85 GiB3,055,958,9763.642mradermacher
I1-IQ3_M2.92 GiB3,132,554,1763.733mradermacher
Q3_K_M3.13 GiB3,357,342,4324.002mradermacher
I1-Q3_K_M3.13 GiB3,357,342,6564.002mradermacher
Q3_K_L3.38 GiB3,627,350,7524.323mradermacher
I1-Q3_K_L3.38 GiB3,627,350,9764.323mradermacher
I1-IQ4_XS3.45 GiB3,700,341,6964.410mradermacher
IQ4_XS3.48 GiB3,731,471,0724.447mradermacher
I1-IQ4_NL3.63 GiB3,894,983,6164.642mradermacher
I1-Q4_03.63 GiB3,895,638,9764.643mradermacher
Q4_K_S3.64 GiB3,909,401,3124.659mradermacher
I1-Q4_K_S3.64 GiB3,909,401,5364.659mradermacher
Q4_K_M3.84 GiB4,126,489,3124.918mradermacher
I1-Q4_K_M3.84 GiB4,126,489,5364.918mradermacher
I1-Q4_13.98 GiB4,278,369,2165.099mradermacher
Q5_K_S4.35 GiB4,673,551,0725.570mradermacher
I1-Q5_K_S4.35 GiB4,673,551,2965.570mradermacher
Q5_K_M4.47 GiB4,798,888,6725.720mradermacher
I1-Q5_K_M4.47 GiB4,798,888,8965.720mradermacher
Q6_K5.13 GiB5,513,312,9926.571mradermacher
I1-Q6_K5.13 GiB5,513,313,2166.571mradermacher
Q8_06.65 GiB7,138,843,8728.508mradermacher
F1612.51 GiB13,431,221,47216.008mradermacher

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 3.52 GiB. The real file is 3.84 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
2560
Vocab
151,936
Sliding window
none
SWA period
MLA
no
Experts
2
Experts per token
1
use_sliding_window
false

Questions people ask

How much VRAM does Luna-7B-A4B need?
Q4_K_M is exactly 4,126,489,312 bytes (3.84 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Luna-7B-A4B'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.
Is Luna-7B-A4B a mixture-of-experts model?
Yes — 2 experts, 1 routed per token. Every expert must be resident, but only the routed ones are read per token, which is why its memory requirement and its speed behave very differently.
Which quantization of Luna-7B-A4B 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.