ATH-MaaS · text · mixture of experts

Marco-Mini-Instruct

ATH-MaaS/Marco-Mini-Instruct

Marco-Mini-Instruct at Q4_K_M is exactly 10,505,424,448 bytes (9.78 GiB / 10.51 GB) — an effective 4.872 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
17.3B
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_S3.33 GiB3,574,629,1841.658mradermacher
I1-IQ1_M3.69 GiB3,958,899,5201.836mradermacher
I1-IQ2_XXS4.28 GiB4,599,350,0802.133mradermacher
I1-IQ2_XS4.76 GiB5,113,545,5362.371mradermacher
I1-IQ2_S4.83 GiB5,183,144,7682.404mradermacher
I1-IQ2_M5.30 GiB5,695,505,2162.641mradermacher
I1-Q2_K_S5.51 GiB5,911,629,6322.741mradermacher
Q2_K5.92 GiB6,351,539,7762.946mradermacher
I1-Q2_K5.92 GiB6,351,540,0322.946mradermacher
I1-IQ3_XXS6.22 GiB6,681,133,8883.098mradermacher
I1-IQ3_XS6.62 GiB7,105,040,1923.295mradermacher
Q3_K_S6.99 GiB7,505,530,4323.481mradermacher
I1-Q3_K_S6.99 GiB7,505,530,6883.481mradermacher
I1-IQ3_S6.99 GiB7,505,530,6883.481mradermacher
I1-IQ3_M7.08 GiB7,597,444,9283.523mradermacher
Q3_K_M7.72 GiB8,291,339,8403.845mradermacher
I1-Q3_K_M7.72 GiB8,291,340,0963.845mradermacher
Q3_K_L8.36 GiB8,981,564,9924.165mradermacher
I1-Q3_K_L8.36 GiB8,981,565,2484.165mradermacher
I1-IQ4_XS8.61 GiB9,240,989,5044.285mradermacher
IQ4_XS8.69 GiB9,335,361,0884.329mradermacher
I1-IQ4_NL9.10 GiB9,774,976,8324.533mradermacher
I1-Q4_09.14 GiB9,812,725,5684.551mradermacher
Q4_K_S9.17 GiB9,850,998,3364.568mradermacher
I1-Q4_K_S9.17 GiB9,850,998,5924.568mradermacher
Q4_K_M9.78 GiB10,505,424,4484.872mradermacher
I1-Q4_K_M9.78 GiB10,505,424,7044.872mradermacher
I1-Q4_110.10 GiB10,842,951,4885.028mradermacher
Q5_K_S11.09 GiB11,910,925,8885.524mradermacher
I1-Q5_K_S11.09 GiB11,910,926,1445.524mradermacher
Q5_K_M11.44 GiB12,287,217,2165.698mradermacher
I1-Q5_K_M11.44 GiB12,287,217,4725.698mradermacher
Q6_K13.21 GiB14,180,372,0326.576mradermacher
I1-Q6_K13.21 GiB14,180,372,2886.576mradermacher
Q8_017.10 GiB18,356,453,9528.513mradermacher

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 9.04 GiB. The real file is 9.78 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
256
Experts per token
8
use_sliding_window
false

Questions people ask

How much VRAM does Marco-Mini-Instruct need?
Q4_K_M is exactly 10,505,424,448 bytes (9.78 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-Mini-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-Mini-Instruct a mixture-of-experts model?
Yes — 256 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-Mini-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.
Marco-Mini-Instruct — VRAM requirements, exact quant sizes — ossmodeldb