Which Local LLM Is Best for Coding? 7 Models Measured Head-to-Head (July 2026, 24GB Edition)

2026年7月15日

When choosing a local LLM (a conversational AI that runs on your own computer; an LLM is a large language model that generates text and code) for coding, what you really want to know is: which one is actually best? I lined them up on my own GPUs and measured. This article focuses on models that fit a 24GB graphics card (GeForce RTX 3090), gives them real code-generation, refactoring, and debugging tasks, and compares pass rates, quality, and speed under the same conditions. All numbers are measured in my environment.

Starting with the conclusion: for coding on 24GB, Ornith-1.0 35B (MoE) is the strongest all-rounder, qwen3-coder:30b wins if speed is your top priority, and qwen3.6 35B-a3b is the versatile pick if you also want general reasoning. The top group is nearly tied on quality — what separates them is speed and stability. Below, the method and the standings in order.

How I measured

Overall ranking (models that fit in 24GB)

A closer look at the top group

First place, Ornith-1.0 35B, passed all 7 practical tasks, aced both code comprehension and reasoning, and still ran at a brisk 95 t/s. It was the only model that placed near the top on every axis I measured. Thanks to MoE (mixture of experts — a design that activates only the parts it needs, letting a large model run in less memory), it fits in 21GB and loads fully onto a 24GB card. If you want one all-round pick, this is the first one to try.

Second place, qwen3-coder 30B, matches the leaders on quality but stands out for sheer speed: 124 t/s, keeping the wait per task down to a few seconds. True to its code-specialized training, it handled output-prediction and bug-hunting tasks without a stumble. If your workflow makes many short calls — editor-completion style — this speed pays off.

Third place, qwen3.6 35B-a3b, is the versatile one, with a perfect score on the 8 reasoning questions. It dropped a question or two on the 10 code-comprehension questions, putting its pure coding ability a step behind the top two. It just barely fits in 24GB and runs fast as long as you keep the context modest. A good candidate if you want one machine to handle both code and prose.

Fourth place, gemma4 31B, posted the highest average task quality of this round. As a dense model, though, it runs at 23 t/s, and the waiting is noticeable. Suited to work where quality comes first and you can live with the speed.

The lightweights hold their own

Fifth place, qwen3 14B, scored perfect marks on both reasoning and code comprehension at just 9.3GB. It missed one practical task, but for its size that’s more than respectable. It loads with room to spare, which makes it an easy everyday choice. Sixth place, Ornith-1.0 9B, also aced code comprehension and is a good pick if you value lightness — with one caveat: on one practical task it fell into a long generation loop and timed out. For work you want to hand off reliably, the larger models were the steadier bet.

Running on a 12GB budget GPU

A GeForce RTX 3060 (12GB) runs the lighter models comfortably. Measured speeds:

What fits in 12GB is roughly the 10GB-and-under class; the 20GB-class 30B–35B models won’t load. The two perfect scorers on code comprehension — qwen3 14B and Ornith-1.0 9B — both run on 12GB, so on a budget GPU these two are the core picks. Note that qwen3 14B exceeds 12GB if you open up a wide context, so I had to keep the context length modest.

Recommendations by use case

Closing thoughts

Within the scope of this test, coding quality was nearly tied across the top group; the real separators were speed and stability. Overall, Ornith-1.0 35B proved the easiest to recommend, qwen3-coder 30B if you want speed, and qwen3.6 35B-a3b as the all-rounder with reasoning to match. With 30B–35B-class models now fitting in 24GB and running fast, home hardware has reached the point where near-professional workflows are realistic. That said, this is a comparison on a limited yardstick of 7 tasks. Real-world feel varies with your languages and tasks, so the surest test is running your own code through them.

Test conditions: Ollama / Q4_K_M / speed is the median of 3 runs generating 256 tokens. Tasks, code, and reasoning tests used deterministic settings. Figures are measured values in my environment (GeForce RTX 3090 / RTX 3060) and will vary with model, quantization, and settings. Mac environments were out of scope for this round.