Wave Field LLM: 1 Billion Parameter Model Successfully Scales

Explore LLM On-Premise

Complete guide to running AI models locally: hardware, stack, and privacy.

MoE Models: The 10 Billion Active Parameter Threshold Between Cost and Performance

Mixture of Experts (MoE) models show a convergence towards approximately 10 billion active parameters, regardless of their total size. This trend is primarily d

Qwen3.5B: a leap forward compared to models from 2 years ago

A Reddit post highlights the progress made in the field of large language models (LLMs). Qwen3.5B, a relatively recent model, shows significantly higher perform

Guide Labs Debuts Interpretable LLM with Steerling-8B

Guide Labs has open-sourced Steerling-8B, an 8 billion parameter large language model (LLM). Its architecture is designed to enhance the interpretability of its

Arcee AI challenges Meta with a 400B parameter open source LLM

The 30-person startup Arcee AI has released Trinity, a 400 billion parameter open source large language model (LLM). The company claims it is one of the largest

Qwen-Scope: Deep Introspection and Granular Control for Qwen 3.5 Models

The Qwen team has unveiled Qwen-Scope, a collection of Sparse Autoencoders (SAEs) designed for the Qwen 3.5 model family. This tool enables mapping and manipula

Local LLMs and agentic workloads: prefill is everything, KV heads beat parameters

Step 3.7 Flash with Claude-style prompts beats Hermes on code: a wake-up call for local LLM deployments

Mistral AI: The open source challenge to OpenAI's dominance

Google's TabFM: zero-shot tabular predictions without training

Longcat 2: INT8 and FP8 quantization now available for on-prem deployment

Why AI Needs a Glossary (and What It Has to Do with On-Premise Deployment)