edge AI model Articles
Browse 10 articles about edge AI model.
LFM3-1.7B-Base: LiquidAI's Tiny Model for Phones and Laptops
LFM3-1.7B-Base runs in under 1GB at 4-bit, handles 256K context, and uses a tri-hybrid architecture built for local, offline AI.
LFM3-1.7B Benchmarks: A 1.7B Model Built for Agentic Tool Use
LFM3-1.7B is a 1.7B-parameter model from Liquid AI tuned for agentic tool calling and local deployment. Here's what it is and who it's for.
LFM3-1.7B Tested: Liquid AI's Tiny Local Agent Model, Where It Fails
Hands-on test of Liquid AI's LFM3-1.7B for tool calling, JSON extraction, and coding, run locally in about 1GB of memory.
LiquidAI LFM3-1.7B: A Local Agent Model That Fits on a Phone
LFM3-1.7B is a 1.73B-parameter edge model running in ~1GB at 4-bit, built for on-device agents, tool calling, and fine-tuning.
Spark-X2.6-4B: The 4B On-Device AI Agent That Punches at 9B
Spark-X2.6-4B is a 4B on-device agent model with a controllable thinking budget and 1M-token context, rivaling 9B rivals.
LFM2.5-24B-A2B: Inside LiquidAI's New Agentic MoE Model
LiquidAI's LFM2.5-24B-A2B packs 24B total/2B active params, 131K context, and strong agentic benchmarks for on-device reasoning.
Needle 3: Running a Tiny On-Device Tool-Calling AI Model
Needle 3 packs tool calling, extraction and embeddings into an 8-29MB file. Here's how the model works and how to deploy it on-device.
Needle 3: The 8-29MB Model Built for On-Device Tool Calling
Needle 3 is an 8-29MB foundation model for on-device tool calling and structured extraction. Here's how its architecture works and how to deploy it.
MiniCPM5-2B: A 2B Open Model That Beats 4B Rivals
MiniCPM5-2B is a 2B-parameter open model from OpenBMB that claims SOTA in its size class and beats larger 4B models on coding and agents.
MiniCPM5-2B: Running OpenBMB's 2B On-Device Model Locally
MiniCPM5-2B packs 2.5B params, 131K context, and 4B-beating benchmarks. Here's how it works and how to run it via GGUF, MLX, or GPTQ.