huihui-ai · text

Huihui-Qwen3-Next-80B-A3B-Instruct-abliterated

huihui-ai/Huihui-Qwen3-Next-80B-A3B-Instruct-abliterated

Huihui-Qwen3-Next-80B-A3B-Instruct-abliterated at Q4_K_M is exactly 48,410,989,216 bytes (45.09 GiB / 48.41 GB) — an effective 4.861 bits per weight, not the nominal 4.

From the file· summed from 1 file(s)
Parameters
79.7B
Architecture
qwen3next
Context
—
native (config.json)
License
mit

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
I1-IQ1_S15.07 GiB16,184,073,1841.625—mradermacher
I1-IQ1_M16.76 GiB17,995,164,6401.807—mradermacher
I1-IQ2_XXS19.57 GiB21,013,650,4002.110—mradermacher
I1-IQ2_XS21.82 GiB23,431,584,7362.353—mradermacher
I1-IQ2_S22.02 GiB23,641,240,5442.374—mradermacher
I1-IQ2_M24.27 GiB26,056,029,1522.616—mradermacher
I1-Q2_K_S25.38 GiB27,251,778,5282.736—mradermacher
Q2_K27.13 GiB29,127,910,0482.925—mradermacher
I1-Q2_K27.13 GiB29,127,910,3682.925—mradermacher
I1-IQ3_XXS28.68 GiB30,796,592,0963.092—mradermacher
I1-IQ3_XS30.48 GiB32,723,610,5923.286—mradermacher
Q3_K_S32.17 GiB34,547,444,3843.469—mradermacher
I1-Q3_K_S32.17 GiB34,547,444,7043.469—mradermacher
I1-IQ3_S32.18 GiB34,549,115,8723.469—mradermacher
I1-IQ3_M32.59 GiB34,991,139,8083.513—mradermacher
Q3_K_M35.57 GiB38,193,703,5843.835—mradermacher
I1-Q3_K_M35.57 GiB38,193,703,9043.835—mradermacher
Q3_K_L38.40 GiB41,233,394,3364.140—mradermacher
I1-Q3_K_L38.40 GiB41,233,394,6564.140—mradermacher
I1-IQ4_XS39.68 GiB42,604,475,3604.278—mradermacher
IQ4_XS40.15 GiB43,108,774,5604.329—mradermacher
I1-Q4_042.17 GiB45,282,710,4964.547—mradermacher
Q4_K_S42.36 GiB45,484,954,2724.567—mradermacher
I1-Q4_K_S42.36 GiB45,484,954,5924.567—mradermacher
Q4_K_M45.09 GiB48,410,989,2164.861—mradermacher
I1-Q4_K_M45.09 GiB48,410,989,5364.861—mradermacher
I1-Q4_146.60 GiB50,037,953,5045.024—mradermacher
Q5_K_S51.22 GiB54,994,916,0005.522—mradermacher
I1-Q5_K_S51.22 GiB54,994,916,3205.522—mradermacher
Q5_K_M52.82 GiB56,710,369,9525.694—mradermacher
I1-Q5_K_M52.82 GiB56,710,370,2725.694—mradermacher
Q6_K61.03 GiB65,528,461,9846.580—mradermacher
I1-Q6_K61.03 GiB65,528,462,3046.580—mradermacher
Q8_078.99 GiB84,812,053,1528.516—mradermacher

Compare with

same modality, comparable size
vs Qwen3-Coder-30B-A3B-Instructvs Qwen3.6-27Bvs Qwen3.8-27Bvs gemma-4-12B-it-qat-q4_0-unquantizedvs gemma-4-E4B-itvs Qwen3-30B-A3B-Thinking-2507All alternatives →Will it fit on my card? →

Will it run on your card?

full quant x context sweep
By VRAM:8GB12GB16GB24GB32GB48GB
Radeon RX 6500 XT 4GBGeForce RTX 3050 6GBGeForce RTX 5050 8GBGeForce RTX 3080 10GBGeForce RTX 2080 Ti 11GBGeForce RTX 5070 12GBGeForce RTX 5060 Ti 16GBApple M3 Pro 18GBGeForce RTX 3080 Ti 20GBGeForce RTX 5090 D V2 24GBGeForce RTX 5090 D 32GBApple M5 Max 36GBApple M5 Pro 48GBApple M5 Max 64GBApple M3 Ultra 96GBApple M5 Max 128GBApple M2 Ultra 192GBApple M3 Ultra 256GBApple M3 Ultra 512GB

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 41.74 GiB. The real file is 45.09 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

Architecture unavailable — this repository is gated and no ungated mirror was found. Exact file sizes above are still authoritative; only the KV math needs the config.

Questions people ask

How much VRAM does Huihui-Qwen3-Next-80B-A3B-Instruct-abliterated need?
Q4_K_M is exactly 48,410,989,216 bytes (45.09 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
Which quantization of Huihui-Qwen3-Next-80B-A3B-Instruct-abliterated 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.