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Llama-3.3-70B-Instruct-abliterated

huihui-ai/Llama-3.3-70B-Instruct-abliterated

Llama-3.3-70B-Instruct-abliterated at Q4_K_M is exactly 42,520,398,912 bytes (39.60 GiB / 42.52 GB) — an effective 4.821 bits per weight, not the nominal 4. Its KV cache at 32K is 10.00 GiB.

From the file· summed from 1 file(s)From the file· KV per layer
Parameters
70.6B
Architecture
llama
80 layers
Context
131,072
native (config.json)
License
llama3.3

Shipped quantizations

● exact bytes, summed from published files
QuantSize●Exact bytes●Effective bpw●Tensors●Publisher
I1-IQ1_S14.29 GiB15,343,488,5121.740—mradermacher
IQ1_M15.60 GiB16,751,201,3441.899—bartowski
I1-IQ1_M15.60 GiB16,751,201,7921.899—mradermacher
IQ2_XXS17.79 GiB19,097,390,1442.165—bartowski
I1-IQ2_XXS17.79 GiB19,097,390,5922.165—mradermacher
IQ2_XS19.69 GiB21,142,113,3442.397—bartowski
I1-IQ2_XS19.69 GiB21,142,113,7922.397—mradermacher
IQ2_S20.71 GiB22,242,348,0962.522—bartowski
I1-IQ2_S20.71 GiB22,242,348,5442.522—mradermacher
IQ2_M22.46 GiB24,119,299,1362.735—bartowski
I1-IQ2_M22.46 GiB24,119,299,5842.735—mradermacher
I1-Q2_K_S22.79 GiB24,471,948,8002.775—mradermacher
Q2_K24.56 GiB26,375,113,7922.991—bartowski
Q2_K24.56 GiB26,375,113,9842.991—mradermacher
I1-Q2_K24.56 GiB26,375,114,2402.991—mradermacher
Q2_K_L25.52 GiB27,401,161,7923.107—bartowski
IQ3_XXS25.58 GiB27,469,499,4563.115—bartowski
I1-IQ3_XXS25.58 GiB27,469,499,9043.115—mradermacher
I1-IQ3_XS27.29 GiB29,307,735,5523.323—mradermacher
Q3_K_S28.79 GiB30,912,056,3843.505—bartowski
Q3_K_S28.79 GiB30,912,056,5763.505—mradermacher
I1-Q3_K_S28.79 GiB30,912,056,8323.505—mradermacher
I1-IQ3_S28.79 GiB30,912,056,8323.505—mradermacher
IQ3_M29.74 GiB31,937,039,4243.621—bartowski
I1-IQ3_M29.74 GiB31,937,039,8723.621—mradermacher
Q3_K_M31.91 GiB34,267,499,5843.886—bartowski
Q3_K_M31.91 GiB34,267,499,7763.886—mradermacher
I1-Q3_K_M31.91 GiB34,267,500,0323.886—mradermacher
Q3_K_L34.59 GiB37,140,597,8244.211—bartowski
Q3_K_L34.59 GiB37,140,598,0164.211—mradermacher
I1-Q3_K_L34.59 GiB37,140,598,2724.211—mradermacher
IQ4_XS35.30 GiB37,902,666,8164.298—bartowski
I1-IQ4_XS35.30 GiB37,902,667,2644.298—mradermacher
IQ4_XS35.64 GiB38,269,668,6084.339—mradermacher
IQ4_NL37.30 GiB40,053,623,8724.542—bartowski
Q4_037.36 GiB40,116,538,4324.549—bartowski
I1-Q4_037.36 GiB40,116,538,8804.549—mradermacher
Q4_K_S37.58 GiB40,347,225,1524.575—bartowski
Q4_K_S37.58 GiB40,347,225,3444.575—mradermacher
I1-Q4_K_S37.58 GiB40,347,225,6004.575—mradermacher

KV cache by context

computed per layer
ContextKV cache (f16)●Flat formulaOverstated byFull / windowed / recurrent
4,0961.25 GiB1.25 GiB—80 / 0 / 0
8,1922.50 GiB2.50 GiB—80 / 0 / 0
16,3845.00 GiB5.00 GiB—80 / 0 / 0
32,76810.00 GiB10.00 GiB—80 / 0 / 0
65,53620.00 GiB20.00 GiB—80 / 0 / 0
131,07240.00 GiB40.00 GiB—80 / 0 / 0

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 36.96 GiB. The real file is 39.60 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json
Layers
80
Attention heads
64
KV heads
8
Head dim
128
Hidden size
8192
Vocab
128,256
Sliding window
none
SWA period
—
MLA
no
Experts
—
Experts per token
—
use_sliding_window
—

Questions people ask

How much VRAM does Llama-3.3-70B-Instruct-abliterated need?
Q4_K_M is exactly 42,520,398,912 bytes (39.60 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is Llama-3.3-70B-Instruct-abliterated's KV cache?
10.00 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 Llama-3.3-70B-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.