FitMyLLM
Liquid AI/Dense

Liquid AILFM2.5 2.6B

LFM2.5 2.6B — Liquid AI's small hybrid model post-trained for agentic work and tool use, with a 128K context. 22 of its 30 layers are short convolutions that keep no KV cache.

chatagentictool_usemultilingual
2.7B
Parameters
128K
Context length
7
Benchmarks
6
Quantizations
69K
HF downloads
Architecture
Dense
Released
2026-08-04
Layers
30
KV Heads
8
Head Dim
64
Family
lfm

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q4_K_M4.89
2.3 GB
2.1 + 0.2 KV
good
Q5_K_S5.57
2.6 GB
2.4 + 0.2 KV
good
Q5_K_M5.7
2.6 GB
2.4 + 0.2 KV
good
Q6_K6.56
2.9 GB
2.7 + 0.2 KV
excellent
Q8_08.5
3.5 GB
3.4 + 0.2 KV
lossless
FP1616
6.1 GB
5.9 + 0.2 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
▸ READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN LFM2.5 2.6B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (7)

LiveCodeBench59.4
GPQA Diamond55.8
AIME51.9
SciCode14.4
HLE6.2
AA Long Context5.7
Terminal-Bench 2.14.5

Run this model

▸Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run lfm:3b-q4_K_M

Tag may need adjustment — check ollama.com/library/lfm for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA Tesla C2050
3 GB VRAM • 144 GB/s
NVIDIA
NVIDIA Tesla M2050
3 GB VRAM • 148 GB/s
NVIDIA
NVIDIA Tesla S2050
3 GB VRAM • 148 GB/s
NVIDIA

Find the best GPU for LFM2.5 2.6B

Build Hardware for LFM2.5 2.6B
▸ SPEC SHEET

LFM2.5 2.6B — 2.7B Dense.

▸ SPECIFICATIONS
PARAMETERS
2.7B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
chat, agentic, tool_use, multilingual
RELEASE DATE
2026-08-04
PROVIDER
Liquid AI
FAMILY
lfm
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q4_K_M4.892.1 GB94%
Q5_K_S5.572.4 GB96%
Q5_K_M5.72.4 GB96%
Q6_K6.562.7 GB97%
Q8_08.53.4 GB100%
FP16165.9 GB100%
§ 01BENCHMARK SCORES
GPQA Diamond55.8
LiveCodeBench59.4
AIME51.9
HLE6.2
aa_scicode14.4
aa_lcr5.7
aa_terminal_bench_v24.5
§ 03COMPATIBLE GPUs
30 @ Q4_K_M