LFM
Open-weight on-device AI models for private, low-latency edge intelligence—free to use under $10M revenue.
LFM2.5 is the most complete open-weight edge AI lineup we've seen—from 230M to 24B, including audio, vision, and Japanese-specific models, all free under $10M revenue. It beats Llama 3.2 and Gemma at the 1B scale on benchmarks like IFEval and AIME25. The catch: you need edge hardware and the willingness to manage your own deployment; this isn't a cloud API. For most builders, the free tier is all you'll ever need.
Verified 5d ago · liveness 79/100 · cite: rightaichoice.com/tools/lfm
- Developers building on-device copilots and local assistants needing private, low-latency AI
- Enterprises deploying AI on edge hardware for privacy-sensitive workflows
- Automotive teams integrating in-car assistants with offline capability
- Japanese-language application developers requiring cultural nuance
- Applications requiring large-scale cloud API calls or massive throughput
- Use cases needing models larger than 24B parameters for complex reasoning
- Teams without edge deployment infrastructure or compatible hardware
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Skip LFM2.5 if you need a managed cloud API with minimal operational overhead, or if your team lacks edge deployment infrastructure and the willingness to handle model optimization and deployment yourself.
Once your company's annual revenue passes $10M, you must purchase a commercial license to continue using the models in production.
LFM2.5's free tier is a generous entry point for startups and developers, with no per-token costs. For companies under $10M revenue, it's effectively $0 for unlimited use, making it cheaper than per-token cloud APIs like GPT-4o or Claude. For larger enterprises, custom pricing scales with deployment size; compare with cloud APIs that charge per token, which can be unpredictable at scale.
In short
LFM — Open-weight on-device AI models for private, low-latency edge intelligence—free to use under $10M revenue. Best for Developers building on-device copilots and local assistants needing private, low-latency AI, Enterprises deploying AI on edge hardware for privacy-sensitive workflows, Automotive teams integrating in-car assistants with offline capability. Free to use.
What's new in LFM
Checked 5 days agoAcross the latest 4 updates: 2 feature updates and 2 launches.
LFM2.5-VL-3B: A Better and Faster Vision-Language Model for the Edge
LFM2.5-VL-3B released, a vision-language model optimized for edge deployment with improved speed and accuracy.
LFM2.5-2.6B: Deploy Agents Everywhere
LFM2.5-2.6B released for agent deployment across edge devices, emphasizing efficiency and portability.
LFM2.5-Encoders: Fast at Long Context, Even on CPU
New encoder models deliver fast long-context performance on CPU, expanding deployment options.
LFM2.5-230M: Built to Run Anywhere
Ultra-small 230M model designed for universal on-device inference with minimal resource footprint.
What people actually say about LFM — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
54 mentions across 4 sources (Reddit, Hacker News, GitHub, Lemmy) · researched Jul 3, 2026.
- +Blazing fast inference speed on CPUs (35-40 t/s on old hardware)
- +Open weights on Hugging Face with permissive commercial license up to $10M
- +Excellent at tool calling and instruction following for simple tasks
- +Very low memory footprint suitable for phones and IoT devices
- +Free API on OpenRouter removes financial barrier to entry
- −Serious coherence issues in larger models (1/20 on user tests)
- −Fails on complex or multi-step instructions on small models
- −Limited community finetunes and ecosystem support on Hugging Face
- −Previous LFM2 models set low expectations for reliability
- −GitHub activity is stale and focused on outdated image generation project
- • No paid tiers announced yet — may incur inference costs if self-hosting on cloud GPUs
- • Commercial license beyond $10M revenue requires separate agreement (pricing not disclosed)
In users’ own words
“We are an experienced PvP Group who play on EST based times, and are known on a few official servers because of our pvp. If intrested add and message me on steam @ http://steamcommunity.com/id/TomatoAim/”
Real posts from independent users, linked to the source — not testimonials we collected.
Viability Score
How well maintained and how widely used is LFM? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Open-weight models for on-device deployment
- On-device text generation with low latency
- Native audio input/output (speech and text)
- Vision-language understanding (multi-image, multilingual)
- Japanese-optimized chat model
- Reasoning model under 1GB memory
- Mixture-of-experts for on-device efficiency (8B-A1B, 24B-A2B)
- Ultra-small models for embedded devices (230M, 350M)
- Fast hybrid-architecture inference on CPU
- Long-context support on CPU via new encoders
- Quantization-aware training (INT4) for audio detokenizer
- Open-weight with no copyleft, free commercial use under $10M revenue
- LFM2.5-2.6B model for agent deployment
- LFM2.5-VL-3B vision-language model for edge
- LFM2.5-230M for minimal-resource devices
About LFM
LFM2.5 is a family of open-weight AI models from Liquid AI, engineered for on-device and edge deployment. The lineup spans from a 230M-parameter model for minimal-resource devices to a 24B mixture-of-experts variant, including specialized models for Japanese, vision-language, and audio-language tasks. With pretraining scaled to 28T tokens and reinforcement learning-based post-training, LFM2.5 models deliver strong performance on knowledge, instruction following, math, and tool use—especially at the 1B scale, where they often outperform larger models. All models run on CPUs, GPUs, and NPUs, with day-zero support for llama.cpp, MLX, vLLM, ONNX, LEAP, and optimized NPU performance from AMD and Nexa AI. Commercial use is free for companies under $10M in annual revenue, with no copyleft, so fine-tunes stay private. For teams building local copilots, in-car assistants, or privacy-sensitive edge workflows, LFM2.5 offers a private, cost-predictable alternative to cloud APIs—no per-token fees, no data leaving the device.
Behind the Verdict
Liquid AI's LFM2.5 family stands out for its breadth and practical focus on edge deployment. Unlike many open-weight models that are simply small versions of cloud-scale models, LFM2.5 is architected for on-device efficiency—hybrid architecture, quantization-aware training, and a range of sizes from 230M to 24B. The 1.2B Instruct model's benchmark results are impressive, often matching or exceeding models four times its size, and the Japanese and audio variants address niches that larger vendors often neglect. The Audio model, processing audio natively, cuts latency versus pipelined ASR+LLM+TTS approaches. Recent releases—LFM2.5-VL-3B, LFM2.5-2.6B, LFM2.5-Encoders, and LFM2.5-230M—show a steady cadence of innovation, expanding the deployment envelope. Where LFM2.5 truly shines is in scenarios where privacy, latency, and cost predictability matter more than raw parameter count. You get full control over the model, can fine-tune on proprietary data without copyleft, and avoid per-token API fees entirely. The free commercial license under $10M annual revenue is generous, and the fact that research, education, and non-profit use is always free is a significant plus. However, this is not a set-and-forget solution. You are responsible for deployment infrastructure, model selection, and optimization. There's no cloud API—if you want to scale beyond edge devices, you need to manage servers yourself. The free tier's $10M revenue cap may be a minor hurdle for fast-growing startups, but it's fair. And while benchmarks are strong, real-world performance varies; you'll need to test on your target hardware. LFM2.5 is an excellent choice for developers and enterprises with edge hardware, especially those in automotive, IoT, or privacy-sensitive domains. It's less suited for teams that want a quick cloud API or lack the technical resources to manage on-prem deployment. Compared to cloud-only alternatives like GPT-4o (now on GPT-5.5) or Claude, LFM2.5 offers a private, cost-predictable edge AI stack—but you trade away the simplicity of an API for the control of open weights.
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Real-world workflow fit
Concrete scenarios for the personas LFM actually fits — and what changes day-one when you adopt it.
You want a private, offline assistant on your laptop.
Outcome: Download LFM2.5-1.2B-Instruct via Hugging Face, run it with llama.cpp on your CPU, and have a responsive copilot within minutes, with no data leaving the device.
You need an in-car assistant with voice and vision.
Outcome: Use LFM2.5-Audio-1.5B and LFM2.5-VL-3B, which run on NPUs and CPUs, to create real-time, offline voice and vision capabilities that work in vehicles.
You want a culturally nuanced chatbot.
Outcome: Fine-tune LFM2.5-1.2B-JP on your proprietary customer data, keep the model private (no copyleft), and deploy it on edge devices for low-latency, context-aware responses.
Use Cases
- Deploy a local copilot on a laptop or mobile device for private productivity.
- Integrate an in-car assistant with real-time voice and vision capabilities.
- Build a Japanese-language customer support chatbot with cultural nuance.
- Run a tool-calling agent on consumer hardware without cloud dependency.
- Create a multimodal edge application for visual and audio understanding.
- Fine-tune a base model on proprietary data for domain-specific edge AI.
Models Under the Hood
as of 2026-08-19
Limitations
- The models are optimized for on-device and edge deployment, with a focus on efficiency and low latency.
- The free commercial license is limited to companies under $10M in annual revenue, with a paid license required above that threshold.
- While models are available on Hugging Face and LEAP, there is no direct cloud API offering; deployment is intended for local or edge environments.
as of 2026-08-19
Verification history
We have re-verified LFM 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published LFM tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Developers, researchers, non-profits, and startups with under $10M annual revenue who want to use open models in production at no cost.
What this tier adds
Starting tier: free download, run, and fine-tune of all open LFM models, with commercial use allowed under the $10M revenue cap.
Enterprise
Custom
Ideal for
Companies over $10M annual revenue needing commercial licensing, bespoke optimization, and deployment support at scale.
What this tier adds
Adds commercial license, bespoke architecture and optimization, OEM and on-prem support, and dedicated support with SLAs.
Where the pricing makes sense
The company stage and team size where LFM's pricing actually pencils out — and where peers do it cheaper.
LFM2.5's free tier is a generous entry point for startups and developers, with no per-token costs. For companies under $10M revenue, it's effectively $0 for unlimited use, making it cheaper than per-token cloud APIs like GPT-4o or Claude. For larger enterprises, custom pricing scales with deployment size; compare with cloud APIs that charge per token, which can be unpredictable at scale.
Setup time & first value
How long it actually takes to get something useful out of LFM — broken out by persona, not the marketing-page minute.
For developers familiar with Hugging Face and llama.cpp, you can have the 1.2B Instruct model running on a CPU in under 10 minutes. For NPU-optimized deployments, allow a few hours of integration work. Larger models or custom fine-tuning may take a day or more.
Switching to or from LFM
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From GPT-4o or Claude API to LFM2.5: Download the open weights, convert to your runtime format (e.g., GGUF for llama.cpp), and migrate prompts and function calling logic to the model's format.
- ↗To GPT-4o or Claude: Export fine-tuned weights, convert to a compatible format, and adjust for the cloud API's context window and rate limits.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with LFM
Common stack mates teams adopt alongside LFM, with the specific reason each pairing earns its keep.
StableLM
StableLM is an open-source, self-hostable LLM suite from Stability AI for transparent text and code generation, with 3B and 7B Alpha models under permissive
Falcon LLM
Open-weight multilingual AI with hybrid Transformer-Mamba architecture from TII.
Ollama
Run open-source LLMs locally with one command, then scale to cloud
Featured Head-to-Head Comparisons
Lfm vs Spider Cloud
Choose LFM if you're deploying AI on edge devices (copilots, assistants, IoT) and need private, low-latency, on-device models. Choose Spider Cloud if your AI agents need real-time web data, scraping, and browser automation. They are complementary rather than direct competitors.
Lfm vs Temporal Ai
Choose LFM if your priority is private, low-latency on-device AI with strong multimodal capabilities under 1.6B parameters. Choose Temporal AI if you need a durable execution platform to make AI agents and workflows crash-proof. They are complementary: LFM handles inference, Temporal handles orchestration.
Lfm vs Presto Voice
Presto Voice and LFM serve entirely different domains: Presto Voice is a specialized drive-thru voice automation platform for QSR chains seeking revenue lift, while LFM offers on-device AI models for developers building private, low-latency edge applications. Buyers should choose based on their need: restaurant operations vs. edge AI development. If you run a QSR chain, Presto Voice is the clear choice; if you’re an AI engineer needing local inference, LFM is the better fit.
Alternatives to LFM
View allStableLM
StableLM is an open-source, self-hostable LLM suite from Stability AI for transparent text and code generation, with 3B and 7B Alpha models under permissive
Falcon LLM
Open-weight multilingual AI with hybrid Transformer-Mamba architecture from TII.
Frequently Asked Questions
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