SShortSingh.
Back to feed

Developers Can Now Run Google Gemma 4 AI Agents Locally Using Docker Model Runner

0
·96 views

A developer has demonstrated how to run an AI agent entirely on a local machine by replacing the cloud-based Gemini 2.5 Flash model with Google's open-weight Gemma 4, served via Docker Model Runner. Docker Model Runner is a built-in Docker Desktop feature that pulls and runs large language models locally, exposing an OpenAI-compatible API on localhost without requiring cloud credentials or API keys. The setup uses Google's Agent Development Kit (ADK) alongside BigQuery MCP tools, with only the model endpoint needing to change from the original cloud deployment. Gemma 4 is available in multiple sizes on Docker Hub, ranging from a 2.94 GiB edge-friendly variant to a 20 GiB high-quality dense model, with the E4B variant recommended as the best balance of speed and quality for local agent development. The approach eliminates per-call API costs and latency during development while keeping the agent logic, tools, and system instructions identical to the production cloud version.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Alpie Seed Tested on 16GB MacBook Pro as Mid-Tier Local AI Model

A video creator testing local models on a 16GB MacBook Pro evaluated Alpie Seed, a post-trained version of Qwen 3.5 9B from Mumbai-based company 169pi. At roughly 5.6 GB, the model ran at about 6 tokens per second and answered a basic dolphin question in around 35 seconds, with notably shorter responses than other models tested. It completed a word-count coding task in under three minutes, producing working code with basic usage notes. The reviewer judged it suitable for everyday office tasks, summarization, and small coding projects, but not for refactoring real codebases or building larger applications. The video is the fourteenth in a series comparing models that run on consumer hardware, with the reviewer noting that most tested models are now practical locally.

0
ProgrammingDEV Community ·

GitHub Actions exit code 137 usually means the kernel killed a step for running out of memory

Exit code 137 corresponds to signal 9 (SIGKILL), typically sent by the Linux out-of-memory killer on a CI runner rather than by a failure in the code itself. The step stops abruptly with no stack trace or error message explaining the cause, and retrying it generally fails again because the memory limit remains unchanged. The problem can appear intermittently, depending on what else ran on the machine beforehand, and a container killed for exceeding its --memory cap reports the same code. Other possible causes include a deliberate SIGKILL from a timeout wrapper or raising a heap limit above the runner's RAM. The fix is to confirm the kill through the kernel log, then reduce peak memory use or move the job to a larger runner.

0
ProgrammingDEV Community ·

Author traces the origins of Internet RFCs, from RFC 1 in 1969 to modern web protocols

The author recounts how working on a 3-D Secure payment integration led them to study RFCs 7515, 7516, and 7519, which define JWS, JWE, and JWT. They note that the JWT libraries used in common Go and Node.js services implement these RFCs, which are numbered sequentially from RFC 1 in 1969 and never change once published. The piece explains that everyday technologies such as IP, TCP, HTTP, TLS, and DNS are all defined in RFCs, and that these documents are what let a web request reach a site. The author also recalls learning about the foundations of the web through Lynx, a text-based browser from 1992, and an interactive version of the essay is available on their site.

0
ProgrammingDEV Community ·

Latchkey Undercuts GitHub Hosted Runners on Price, But Break-Even Sits Near 7,700 Minutes

Comparing per-minute rates, Latchkey charges $0.0025 for a 2 vCPU Linux minute versus GitHub's $0.006, roughly 58% less. Latchkey requires a monthly plan fee, while GitHub includes a monthly usage allowance, so the two costs equalize at about 7,714 Linux minutes per month. Below that volume GitHub remains cheaper, and on public repositories its standard runners are free. GitHub also offers broader platform support, including Windows and macOS, and larger runners up to 96 vCPU, whereas Latchkey currently supports only Ubuntu on x86_64 at 2 to 16 vCPU. Latchkey's distinguishing feature is automatic diagnosis and in-run retry of mechanical job failures.