ATH-MaaS · text · mixture of experts

Marco-Nano-Instruct

ATH-MaaS/Marco-Nano-Instruct

Marco-Nano-Instruct at Q4_K_M is exactly 5,371,989,536 bytes (5.00 GiB / 5.37 GB) — an effective 5.371 bits per weight, not the nominal 4. Its KV cache at 32K is 3.50 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
8.0B
total, not active
Architecture
qwen3moe
28 layers
Context
32,768
native (config.json)
License
apache-2.0

Shipped quantizations

exact bytes, summed from published files
QuantSizeExact bytesEffective bpwTensorsPublisher
I1-IQ1_S2.44 GiB2,623,243,0402.623mradermacher
I1-IQ1_M2.56 GiB2,745,041,6962.744mradermacher
I1-IQ2_XXS2.75 GiB2,948,039,4562.947mradermacher
I1-IQ2_XS2.90 GiB3,112,272,6723.112mradermacher
I1-IQ2_S2.91 GiB3,120,530,2083.120mradermacher
I1-IQ2_M3.06 GiB3,282,928,4163.282mradermacher
Q2_K3.11 GiB3,340,291,6163.340mradermacher
I1-Q2_K3.11 GiB3,340,291,8723.340mradermacher
I1-Q2_K_S3.13 GiB3,365,556,0003.365mradermacher
I1-IQ3_XXS3.35 GiB3,596,944,1603.596mradermacher
I1-IQ3_XS3.43 GiB3,684,355,8723.684mradermacher
Q3_K_S3.60 GiB3,868,085,7923.867mradermacher
I1-IQ3_S3.60 GiB3,868,086,0483.867mradermacher
I1-Q3_K_S3.60 GiB3,868,086,0483.867mradermacher
I1-IQ3_M3.65 GiB3,913,994,0163.913mradermacher
Q3_K_M3.92 GiB4,205,039,1364.204mradermacher
I1-Q3_K_M3.92 GiB4,205,039,3924.204mradermacher
Q3_K_L4.07 GiB4,369,731,1044.369mradermacher
I1-Q3_K_L4.07 GiB4,369,731,3604.369mradermacher
I1-IQ4_XS4.10 GiB4,404,825,8884.404mradermacher
IQ4_XS4.15 GiB4,456,140,3204.455mradermacher
I1-IQ4_NL4.26 GiB4,569,976,6084.569mradermacher
I1-Q4_04.27 GiB4,587,081,5044.586mradermacher
Q4_K_S4.57 GiB4,906,896,9284.906mradermacher
I1-Q4_K_S4.57 GiB4,906,897,1844.906mradermacher
I1-Q4_14.71 GiB5,059,923,7445.059mradermacher
Q4_K_M5.00 GiB5,371,989,5365.371mradermacher
I1-Q4_K_M5.00 GiB5,371,989,7925.371mradermacher
Q5_K_S5.32 GiB5,709,516,3205.708mradermacher
I1-Q5_K_S5.32 GiB5,709,516,5765.708mradermacher
Q5_K_M5.69 GiB6,110,580,2566.109mradermacher
I1-Q5_K_M5.69 GiB6,110,580,5126.109mradermacher
Q6_K6.71 GiB7,209,635,3607.208mradermacher
I1-Q6_K6.71 GiB7,209,635,6167.208mradermacher
Q8_07.94 GiB8,527,233,5688.526mradermacher
F1614.92 GiB16,022,299,16816.019mradermacher

KV cache by context

computed per layer
ContextKV cache (f16)Flat formulaOverstated byFull / windowed / recurrent
4,0960.44 GiB0.44 GiB28 / 0 / 0
8,1920.88 GiB0.88 GiB28 / 0 / 0
16,3841.75 GiB1.75 GiB28 / 0 / 0
32,7683.50 GiB3.50 GiB28 / 0 / 0
65,5367.00 GiB7.00 GiB28 / 0 / 0
131,07214.00 GiB14.00 GiB28 / 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 4.19 GiB. The real file is 5.00 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
28
Attention heads
16
KV heads
8
Head dim
128
Hidden size
1024
Vocab
151,936
Sliding window
none
SWA period
MLA
no
Experts
232
Experts per token
8
use_sliding_window
false

Questions people ask

How much VRAM does Marco-Nano-Instruct need?
Q4_K_M is exactly 5,371,989,536 bytes (5.00 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Marco-Nano-Instruct's KV cache?
3.50 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 Marco-Nano-Instruct a mixture-of-experts model?
Yes — 232 experts, 8 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 Marco-Nano-Instruct 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.