One-GPU Weekend Agent: Start With an 8B Model

You have one consumer GPU, a few hours, and no appetite for a metered API bill. The one-GPU agent test is simple: choose the smallest tool-calling model that fits your GPU, scope it to one task, and treat vendor benchmarks as a ceiling, not a promise. It is a weekend gate before you spend money on a bigger setup. Granite 4.2 is a strong first candidate for a one-GPU weekend test.

Start with the 8B model

IBM Research made Granite 4.2 available on August 25, 2026, as 3B, 8B, and 30B dense decoder-only reasoning models with Apache 2.0 licensing. The 8B model is intended to run on a single GPU, the 3B model is intended for laptops, and the 30B model is intended for enterprise infrastructure. For a weekend bench, the 8B model is the size to test first.

The 3B model is not just smaller. Granite 4.2's 8B and 30B models received an agentic reinforcement-learning stage for software engineering, terminal use, and web search, while the 3B model did not. Larger models are often more capable, but they also ask for more infrastructure, more tuning, and more patience. The 8B model gives you a realistic first pass at a self-hosted agent without pushing you into a long setup.

Fit the quantized weights

Before you install anything, check whether the weights fit your GPU. IBM provided FP8, NVFP4, MXFP4, and GGUF quantized versions of Granite 4.2 for local and edge use. Those builds matter for local inference with limited memory. Your GPU has a viable weekend setup when it can hold the quantized 8B model plus the context you need. The token bill stays small for now.

All three Granite 4.2 models were pretrained from scratch on about 15 trillion tokens and have context windows reaching 512,000 tokens. The 512,000-token window is not the weekend default. Use the long context as a reserve. Do not assume that a model name is enough. The same model behaves differently depending on the quantized format, the software that runs it, and the amount of context you keep open. Start with a short context and a simple task, then expand only if the machine stays comfortable. When the run slows down or crashes, reduce the context length and retest before changing the model.

Scope one task first

The agentic portion of Granite 4.2's supervised fine-tuning data was 69% software engineering, with smaller shares for tool calling, terminal use, math, search, and action, so a code helper is a natural first task. A broad assistant that must search, summarize, and act across many tools needs a smaller scope. A narrow task gives you a cleaner signal in a short build window.

Your weekend agent needs a focused job and a clear stop condition. It can call a tool, return an answer, and stop. It does not need to manage a long history or a complex workflow. The stop rule is part of the design. A model that finishes a small job is easier to debug than one that keeps asking. Too broad a task makes the model look weak and turns the weekend into scope debugging instead of building something useful. You can judge the fit by the task size, the tool count, and the stop condition.

Treat benchmarks as a ceiling

Granite 4.2's vendor-reported benchmark results show the 30B model at 57.00 on SWE-Bench Verified and 29.24 on Terminal-Bench 2.1, with the 8B model at 47.67 and 20.56 on the same tests. Use those numbers to rank options, then test the actual job. Your weekend task will be narrower, messier, and less standardized than a benchmark suite. The 30B's higher scores are not the result you should expect on your task. The real question is whether the model does the job reliably in your setup.

Do not let a high score replace a small experiment. Benchmarks show strength at a class of task. They cannot tell you whether your prompt, tools, and data will behave the same way. For a weekend agent, the useful test is simple: give the model one narrow job, a small set of tools, and a stop condition. See whether it finishes without constant correction. Clean finishes give you a base; messy ones mean you adjust the task before blaming the model.