How much memory do I need to run LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX (IQ4_XS)?

The file size is 1.52 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 2.45 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.

Does LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?

8 GB: up to BF16 (est. 6.34 GB); 12 GB: up to BF16 (est. 6.34 GB); 16 GB: up to BF16 (est. 6.34 GB); 24 GB: up to BF16 (est. 6.34 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).

What is the command to run LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX with Ollama?

Run "ollama run hf.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.

DavidAU

LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX

Parameters: 2.7B
Max context length: 131,072
Architecture: lfm2
License: Conditional

Base model: LiquidAI/LFM2.5-2.6B
License: apache-2.0
llama.cpp: b5873+

Memory needed to run IQ4_XS at 8K tokens

2.45 GB Estimated

File size 1.52 GB Measured

VRAM it fits in (rough guide)

8 GB fits
12 GB fits
16 GB fits
24 GB fits
32 GB fits
48 GB fits

See devices and models for 4 GB

Macs cap how much memory the GPU can use, so leave headroom in unified memory.

Data as of Oct 31, 2026

About LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX

LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX is a GGUF model based on LiquidAI/LFM2.5-2.6B on Hugging Face. GGUF files are published by DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF. It has about 2.7B parameters, the lfm2 architecture, a maximum context length of 131,072 tokens (values written in the GGUF).

There are 4 quantizations, with file sizes from 1.52 GB (IQ4_XS) to 5.40 GB (BF16) (actual sizes on Hugging Face).

Running IQ4_XS (1.52 GB) with an 8,192-token context needs an estimated 2.45 GB of memory, and an estimated 2.85 GB at 32,768 tokens. For 8,192 tokens, a GPU with 4 GB or more of VRAM (such as Arc A310, Arc A370M), or a Mac with 4 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 LFM Open License v1.0, 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

DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF

Published by the original developer About this label

Last checked: Oct 31, 2026

Architecture: lfm2; llama.cpp b5873 and later
Parameters: 2.7B
Max context length: 131,072
License: lfm1.0 Conditional

File size and memory per quantization for LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX (DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF)
Quantization	File size Measured	Quality guide	4,096	8,192	16,384	32,768	65,536	131,072 max
IQ4_XS LFM2.5-2.6B-Q3.8-TBrilliance-NEO-IQ4_XS	1.52 GB	Small	2.38 GB	2.45 GB	2.59 GB	2.85 GB	3.39 GB	4.46 GB
IQ4_XS LFM2.5-2.6B-Q3.8-TBrilliance-NEO-MAX-IQ4_XS	1.83 GB	Small	2.69 GB	2.76 GB	2.89 GB	3.16 GB	3.70 GB	4.77 GB
Q6_K LFM2.5-2.6B-Q3.8-TBrilliance-NEO-Q6_K	2.22 GB	Very small	3.09 GB	3.16 GB	3.29 GB	3.56 GB	4.09 GB	5.17 GB
Q6_K LFM2.5-2.6B-Q3.8-TBrilliance-NEO-MAX-Q6_K	2.53 GB	Very small	3.40 GB	3.46 GB	3.60 GB	3.87 GB	4.40 GB	5.48 GB
Q8_0 LFM2.5-2.6B-Q3.8-TBrilliance-NEO-Q8_0	2.87 GB	Nearly identical	3.74 GB	3.81 GB	3.94 GB	4.21 GB	4.75 GB	5.82 GB
Q8_0 LFM2.5-2.6B-Q3.8-TBrilliance-NEO-MAX-Q8_0	3.12 GB	Nearly identical	3.99 GB	4.05 GB	4.19 GB	4.46 GB	4.99 GB	6.07 GB
BF16	5.40 GB	Original precision	6.27 GB	6.34 GB	6.47 GB	6.74 GB	7.28 GB	8.35 GB

Ollama  ollama run hf.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

llama.cpp  llama-server -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

Generation speed on common GPUs and Macs (estimated)

A rough guide to how fast IQ4_XS (1.52 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
Arc A370M 4 GB	112 GB/s	44–59	74
Arc A770 (8GB) 8 GB	512 GB/s	202–270	337
GeForce RTX 5090 32 GB	1,792 GB/s	708–944	1,181
Apple M2 8 GB	100 GB/s	40–53	66
Apple M2 Pro 16 GB	200 GB/s	79–105	132
Apple M5 Ultra 96 GB	1,200 GB/s	474–632	791

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

LFM Open License v1.0 Conditional

Commercial use by organizations with annual revenue of USD 10 million or more is not permitted under this license (a separate agreement is needed). Below that, commercial use is allowed.

Based on the license on the original model's Hugging Face page. This differs from the GGUF repository's label (apache-2.0). Quantized files are made from the original model, so check the original model's license terms as well. 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

Mainline llama.cpp can load this model's format (lfm2) from b5873 (Jul 11, 2025) onward. Ollama and LM Studio bundle llama.cpp internally, so an older app fails to load it with errors such as "unknown model architecture". If that happens, update the app.

Example commands using IQ4_XS. 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/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

llama.cpp

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

llama-server -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

LM Studio

Paste DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-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 LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX (IQ4_XS)?: The file size is 1.52 GB (measured). With a 8,192-token context, the estimated memory including the KV cache is 2.45 GB. The formula is published at https://localllm.az-arte.com/en/guide/memory-calculation.
Can LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX be used commercially?: The original model's license is LFM Open License v1.0 (Commercial use with conditions).Commercial use by organizations with annual revenue of USD 10 million or more is not permitted under this license (a separate agreement is needed). Below that, commercial use is allowed. Check the original license text before deciding.
Does LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX run in 8 GB, 12 GB, 16 GB or 24 GB of VRAM?: 8 GB: up to BF16 (est. 6.34 GB); 12 GB: up to BF16 (est. 6.34 GB); 16 GB: up to BF16 (est. 6.34 GB); 24 GB: up to BF16 (est. 6.34 GB) (estimated at 8,192 tokens with an f16 KV cache, files from the most-downloaded source).
What is the command to run LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX with Ollama?: Run "ollama run hf.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS" to use the GGUF on Hugging Face directly. To use another quantization, replace the name at the end.