Guide

Best Local LLMs for Coding

If you want the best local LLM for coding, start with the task you actually care about: writing patches, fixing bugs, and following instructions under tight VRAM limits. The right model is usually the one that stays fast enough to stay in your loop while still producing code you would ship.

Section 1

What matters for coding work

Coding models need to hold context, respect instructions, and produce code that compiles cleanly. A model that is slightly slower but more reliable is usually better than a fast model that needs repeated rewrites.

When you compare candidates, look at the coding scenario results first and then sanity-check the model on the hardware page to make sure it fits your VRAM budget.

  • Quality: does the model follow the task and preserve surrounding code?
  • Latency: can you keep iterating without waiting too long for each response?
  • Memory: does the model fit comfortably on your GPU without spillover?
Browse model benchmark data
Compare model-level speed, quality, and hardware compatibility.
Read the benchmark methodology
See how coding quality and performance are measured.

Section 2

Practical recommendation by VRAM tier

On 8GB-class cards, stay conservative and prefer efficient 7B or 8B coder models with quantization that leaves room for the prompt and runtime overhead.

On 12GB to 16GB cards, you can usually move up to stronger 14B-class models and get a better balance of correctness and responsiveness.

On 24GB and above, the recommendation shifts toward larger models when quality matters more than absolute speed.

  • 8GB: keep the model compact and avoid unnecessary context bloat.
  • 12GB to 16GB: aim for the best quality model that still leaves headroom.
  • 24GB+: use the extra memory to buy quality and reduce compromise.
Check hardware compatibility
See how different GPUs handle the same model families.
Open benchmark results
Inspect raw benchmark rows for the fastest and strongest runs.

Section 3

How to choose the final candidate

Shortlist two or three models, compare them on the benchmark pages, and keep the one that gives you the least friction in real work. The best coding model is the one you can use every day, not the one that looks best in a chart alone.

  • Use a coding-focused benchmark first.
  • Confirm the model fits your GPU with room to spare.
  • Prefer stable, repeatable results over one-off peaks.