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legraphista

Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat

Parameters
70.6B
Max context length
8,192
Architecture
llama
License
Conditional
Base model
failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5
License
llama3

About Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat

Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat is a GGUF model based on failspy/Meta-Llama-3-70B-Instruct-abliterated-v3.5 on Hugging Face. GGUF files are published by legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF. It has about 70.6B parameters, the llama architecture, a maximum context length of 8,192 tokens (values written in the GGUF).

There are 25 quantizations, with file sizes from 15.3 GB (IQ1_S) to 141 GB (BF16) (actual sizes on Hugging Face).

Running IQ1_S (15.3 GB) with an 8,192-token context needs an estimated 18.8 GB of memory. For 8,192 tokens, a GPU with 24 GB or more of VRAM (such as GeForce RTX 3090 Ti, GeForce RTX 4090), or a Mac with 24 GB or more of unified memory, is a rough guide (macOS caps how much memory the GPU can use, so leave some headroom).

The original model is licensed under Meta Llama 3 Community License, which allows commercial use with conditions such as scale or purpose. Check the original license for the terms of use.

File size and estimated memory per quantization

  • Up to 8 GB
  • Up to 16 GB
  • Up to 24 GB
  • Up to 48 GB
  • Up to 128 GB
  • More

File sizes are the actual values on Hugging Face. Memory is estimated for each context length (KV cache f16). The formula is in How memory is calculated.

All memory figures are Estimated (KV cache f16). It should run on a setup with at least this much VRAM (or Mac unified memory). Click a value to see the devices and models that fit that amount (Find by GPU / Mac).

About the "quality guide" (how far a quantization drifts from the original)

For each quantization type, how far the output drifts from the original model, grouped into six levels from values llama.cpp measured on Llama-3-8B.

The value is mean KLD (the difference between the original and quantized model in next-token probabilities); smaller means closer to the original. Quantizations made with an imatrix (importance data) come out slightly smaller. Levels use the value without an imatrix (or with one, for types that only have that).

These values were not measured on this model. Larger models and MoE models tend to drift less with the same type. Distributor-specific mixed types (such as UD-Q4_K_XL) cannot be matched by type name alone, so they show "-".

Quantization Quality guide KLD (no imatrix) KLD (imatrix) Size (Llama-3-8B)
Q8_0 Nearly identical 0.0014 - 7.96 GiB
Q6_K Very small 0.0055 - 6.14 GiB
Q5_K_M Very small 0.0108 - 5.33 GiB
Q5_K_S Small 0.0166 - 5.21 GiB
Q5_1 Small 0.0180 - 5.65 GiB
Q5_0 Small 0.0222 - 5.21 GiB
Q4_K_M Small 0.0313 0.0282 4.58 GiB
Q4_K_S Medium 0.0431 0.0320 4.37 GiB
IQ4_NL Small - 0.0357 4.35 GiB
IQ4_XS Small - 0.0363 4.14 GiB
Q4_1 Medium 0.0717 - 4.78 GiB
Q4_0 Medium 0.0719 - 4.34 GiB
Q3_K_L Medium 0.0872 0.0731 4.03 GiB
Q3_K_M Medium 0.1019 0.0844 3.74 GiB
IQ3_M Medium - 0.1025 3.53 GiB
IQ3_S Medium - 0.1113 3.42 GiB
IQ3_XS Large - 0.1387 3.28 GiB
IQ3_XXS Large - 0.1836 3.05 GiB
Q3_K_S Large 0.2282 0.1998 3.41 GiB
IQ2_M Large - 0.3260 2.74 GiB
Q2_K Very large 0.4451 0.3322 2.96 GiB
Q2_K_S Very large - 0.4034 2.96 GiB
IQ2_S Very large - 0.4392 2.56 GiB
IQ2_XS Very large - 0.5463 2.43 GiB
IQ2_XXS Very large - 0.8120 2.24 GiB
IQ1_M Very large - 1.3931 2.01 GiB
IQ1_S Very large - 2.2546 1.88 GiB

Source: llama.cpp (MIT), measured on Llama-3-8B against FP16, Wikitext-2, revision f364eb6f. Original table · Reading and choosing quantizations

GGUF source

legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF

Published by the original developer Modified version (name includes abliterated etc.) About this label

Last checked: Nov 2, 2026

Architecture
llama
Supported before llama.cpp b4409
Parameters
70.6B
Max context length
8,192
License
llama3 Conditional
File size and memory per quantization for Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat (legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF)
Quantization File size Measured Quality guide 4,096 8,192 max
IQ1_S 15.3 GB Very large 17.5 GB 18.8 GB
IQ1_M 16.8 GB Very large 18.9 GB 20.2 GB
IQ2_XXS 19.1 GB Very large 21.2 GB 22.6 GB
IQ2_XS 21.1 GB Very large 23.3 GB 24.6 GB
IQ2_S 22.2 GB Very large 24.4 GB 25.7 GB
IQ2_M 24.1 GB Large 26.3 GB 27.6 GB
Q2_K_S 24.5 GB Very large 26.6 GB 28.0 GB
Q2_K 26.4 GB Very large 28.5 GB 29.9 GB
IQ3_XXS 27.5 GB Large 29.6 GB 31.0 GB
IQ3_XS 29.3 GB Large 31.4 GB 32.8 GB
Q3_K_S 30.9 GB Large 33.1 GB 34.4 GB
IQ3_S 30.9 GB Medium 33.1 GB 34.4 GB
IQ3_M 31.9 GB Medium 34.1 GB 35.4 GB
Q3_K 34.3 GB - 36.4 GB 37.8 GB
Q3_K_L 37.1 GB Medium 39.3 GB 40.6 GB
IQ4_XS 37.9 GB Small 40.0 GB 41.4 GB
IQ4_NL 40.1 GB Small 42.2 GB 43.5 GB
Q4_K_S 40.3 GB Medium 42.5 GB 43.8 GB
Q4_K 42.5 GB - 44.7 GB 46.0 GB
Q5_K_S (2 parts) 48.7 GB Small 50.8 GB 52.1 GB
Q5_K (2 parts) 49.9 GB - 52.1 GB 53.4 GB
Q6_K (3 parts) 57.9 GB Very small 60.0 GB 61.4 GB
Q8_0 (3 parts) 75.0 GB Nearly identical 77.1 GB 78.5 GB
F16 (6 parts) 141 GB Original precision 143 GB 145 GB
BF16 (6 parts) 141 GB Original precision 143 GB 145 GB 
Ollama  ollama run hf.co/legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:IQ1_S
llama.cpp  llama-server -hf legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:IQ1_S

Generation speed on common GPUs and Macs (estimated)

A rough guide to how fast IQ1_S (15.3 GB) generates text. Each token reads the model weights once, so the upper limit is memory bandwidth ÷ file size. The guide is 60–80% of that, where most published measurements fall.

Device Memory bandwidth Guide (tokens/s) Estimated Upper limit
Radeon RX 7900 XT 20 GB 800 GB/s 31–42 52
Radeon RX 7900 XTX 24 GB 960 GB/s 38–50 63
GeForce RTX 5090 32 GB 1,792 GB/s 70–93 117
Apple M2 24 GB 100 GB/s 3.9–5.2 6.5
Apple M2 Pro 32 GB 200 GB/s 7.8–10 13
Apple M5 Ultra 96 GB 1,200 GB/s 47–63 78

Estimates only. Prompt processing speed (limited by compute) is not included. Longer contexts and offloading part of the model to the CPU make it slower. Macs are shown only when installed memory is at least 4/3 of what is needed. Devices without published bandwidth (some GeForce cards, etc.) are not shown.

License and commercial use

Meta Llama 3 Community License Conditional

Allows commercial use. Over 700 million monthly active users requires a separate license from Meta. Redistribution and published derivatives must show "Built with Meta Llama 3", and you must follow the Acceptable Use Policy.

Based on the license on the original model's Hugging Face page. Original license text (checked Oct 18, 2026)

The summary states facts this site read from the original text and is not legal advice. Always check the original license and its latest terms before use. All licenses

How to run

Example commands using IQ1_S. To use another quantization, replace the name with one from the table above. How to run local LLMs

Ollama

Run it by pointing directly at the GGUF on Hugging Face.

ollama run hf.co/legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:IQ1_S

llama.cpp

Pass the repository and quantization name to llama-server (or llama-cli) with -hf.

llama-server -hf legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:IQ1_S

LM Studio

Paste legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF into the search box, download it, and choose the quantization from the file list.

Frequently asked questions

How much memory do I need to run Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat (IQ1_S)?
The file size is 15.3 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 18.8 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
Can Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat be used commercially?
The original model's license is Meta Llama 3 Community License (Commercial use with conditions).Allows commercial use. Over 700 million monthly active users requires a separate license from Meta. Redistribution and published derivatives must show "Built with Meta Llama 3", and you must follow the Acceptable Use Policy. Check the original license text before deciding.
Does Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?
8 GB: no quantization fits; 12 GB: no quantization fits; 16 GB: no quantization fits; 24 GB: up to IQ2_S (est. 25.7 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
What is the command to run Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat with Ollama?
Run "ollama run hf.co/legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:IQ1_S" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.