LFM2.5 2.6B
By Liquid AI · United States
Updated 2026-08-28
Overview
LFM2.5 2.6B is Liquid AI's ultra-compact dense model with a 128K context window and multilingual support, running in just 1.5GB VRAM at Q4 for CPU and edge use.
When to pick this model
- Multilingual chat on extremely resource-constrained devices
- Mobile or embedded deployments needing long-context handling in minimal memory
- CPU-only inference where even 7B models are too heavy
- Prototyping edge AI features with a long context budget
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 1.5 GB |
| Q5_K_M | 1.8 GB |
| Q8_0 | 2.8 GB |
| FP16 (no quantization) | 5 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, LFM2.5 2.6B fits an 8 GB consumer card at Q4_K_M (1.5 GB). Stepping up to Q8_0 nearly doubles the footprint to 2.8 GB, and unquantized FP16 weights take 5 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, LFM2.5 2.6B needs roughly 3.4 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 50 tokens/sec on entry-level GPUs, on the order of 85 tokens/sec on a mid-range card, and up to 130 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches LFM2.5 2.6B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for LFM2.5 2.6B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | FP16 (5 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (5 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (5 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (5 GB used) |
| 32 GB | RTX 5090 | FP16 (5 GB used) |
Which GPU should you buy to run LFM2.5 2.6B?
To run LFM2.5 2.6B locally at Q4, you need ~1.5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Ultra-compact: 1.5GB VRAM at Q4
- Native 128K context
- Multilingual support
- Optimized for CPU/edge inference
Limitations
- LFM Open License v1.0 — review terms for commercial use
- No Ollama tag; install via Hugging Face
- Small model size limits code and complex-reasoning performance
Typical workloads
In our catalog grid, LFM2.5 2.6B is filed under Multilingual Chat, CPU Inference, Edge/Mobile — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the LFM Open License v1.0 license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Liquid Foundation Model · dense · 2.6B parameters · 128K context
Training: Liquid AI: LFM architecture (an alternative to classic Transformers) optimized for memory efficiency and CPU/edge inference.
The smallest LFM2.5 variant — long context and multilingual support in under 2GB of VRAM.
Quick start
# HuggingFace : LiquidAI/LFM2.5-2.6BOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does LFM2.5 2.6B need?
At the recommended Q4_K_M quantization, LFM2.5 2.6B needs about 1.5 GB of VRAM. Q8_0 takes 2.8 GB, and unquantized FP16 weights take 5 GB.
Can LFM2.5 2.6B run without a GPU?
Yes — with roughly 3.4 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does LFM2.5 2.6B support?
LFM2.5 2.6B supports a 125k-token context window (128,000 tokens).
Can I use LFM2.5 2.6B commercially?
LFM2.5 2.6B ships under the LFM Open License v1.0 license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is LFM2.5 2.6B on consumer hardware?
Our compatibility engine estimates on the order of 85 tokens/sec on a mid-range GPU and up to 130 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of LFM2.5 2.6B should I download first?
Start with Q4_K_M (1.5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at FP16.