Gemma4 26B-A4B Delivers Fast Local Performance with Web Search and Image Support
Gemma4 26B-A4B Performance and Features
The gemma-4-26B-A4B model demonstrates strong performance for local use, with the source reporting speeds of approximately 145 tokens per second when running on an RTX 4090 GPU. This combination of capability and speed makes it suitable for responsive local applications.
Key Features from Source
- Model: gemma-4-26B-A4B
- Performance: ~145 t/s (tokens per second) on RTX 4090
- Integration: Web search MCP (Model Context Protocol) support
- Multimodal: Image support included
- Platforms: Setup documented for Mac and iPhone usage
The source mentions that the experience can be improved with simple tricks and a short system prompt, though specific details about these optimizations are not provided in the excerpt. The author has documented their complete setup process in a blog post that covers configuration and usage across multiple devices.
For developers interested in implementing this setup, the full configuration details, system prompts, and optimization techniques are available in the referenced blog post at the provided URL.
📖 Read the full source: r/LocalLLaMA
👀 See Also
Developer builds .NET SaaS template generator with Claude Code, shares workflow insights
A developer open-sourced NETrock, a .NET 10 SaaS starter template with authentication, ORM, and background jobs, then built a client-side generator for it using Claude Code. The generator lets users pick features and download a working .zip project that stays in their browser.
Coasts: Containerized Hosts for Running Multiple Localhost Environments
Coasts is a Docker-in-Docker solution that solves the problem of running multiple localhost environments simultaneously, handling port conflicts, secrets, and volume topologies without requiring complex scripting.
CC-Canary: Detect Regressions in Claude Code with Local JSONL Analysis
CC-Canary reads Claude Code session logs and produces a forensic report on model drift, including read:edit ratio, reasoning loops, cost trends, and auto-detected inflection dates.
User Experience: Switching from OpenClaw to Hermes Agent on Local LLM
A developer reports switching from OpenClaw to Hermes Agent using Qwen3.5-9B on an RX 9070 XT with 16GB VRAM. Hermes completed a complex task with 5 correct tool calls versus OpenClaw's 50+ steps, running 2:30 minutes faster while maintaining RAG, tool calling, and persistent memory functionality.