What is the best local LLM for coding in 2026?

For a 24 GB GPU, KAT-Coder-V2.5-Dev and Qwen3-Coder-30B-A3B are the standouts: both are Mixture-of-Experts coders that fit entirely on the card at Q5 (about 21-23 GB), and KAT-Coder scores 69.4 on SWE-bench Verified on its own model card. If you have 32 GB, the dense Qwen2.5-Coder-32B is a top choice for raw code quality. On 8 GB, Qwen2.5-Coder-7B is the best small coder.

What is the best local coding model for an 8 GB GPU?

Qwen2.5-Coder-7B is the best coding model that fits an 8 GB graphics card, needing about 6 GB at a Q5 quantization including KV cache. It runs entirely on the GPU and is genuinely useful for everyday coding tasks.

Can I run a coding model on a 24 GB GPU?

Yes, and this is the sweet spot for local coding. A 24 GB card such as an RTX 3090, 4090 or 5090 runs KAT-Coder-V2.5-Dev (about 23 GB at Q5_K_S) or Qwen3-Coder-30B-A3B (about 21 GB at Q5_K_XL) entirely in VRAM, giving you an agentic coder with a long context on a single consumer GPU.

Are Mixture-of-Experts coding models more memory-efficient?

MoE coders like KAT-Coder and Qwen3-Coder are faster, not smaller. Only a few experts run per token, which speeds up inference, but every expert must still be held in memory. Their VRAM footprint is set by the total parameter count, not the active one.

Best Local LLM for Coding (2026)

By Lefi Abdelmonem

Author · AI Local Check

A local coding model has to clear a higher bar than a chat model: it needs strong reasoning, a long context to hold your files, and — the part most guides skip — it has to actually fit on your machine. This page pairs the strongest open coding models of 2026 with the hardware that runs them, using memory figures measured from each model's real GGUF files.

The short version: if you have a 24 GB GPU, you can run a genuinely capable agentic coder entirely on the card. If you have 8–12 GB, you can still run a very useful 7B–14B coder. Here's the breakdown.

The best local coding models, by hardware

Model	Size	Fits on	Quant & memory
KAT-Coder-V2.5-Dev	34.7B MoE	24 GB GPU	Q5_K_S ≈ 23.3 GB
Qwen3-Coder-30B-A3B	30.5B MoE	24 GB GPU	Q5_K_XL ≈ 21.4 GB
Qwen2.5-Coder-32B	32.8B dense	32 GB GPU	Q3_K_M ≈ 31.4 GB
DeepSeek-Coder-V2-Lite	15.7B MoE	12–16 GB GPU	Q4_K_L ≈ 11.8 GB
Qwen2.5-Coder-7B	7.6B dense	8 GB GPU	Q5_0 ≈ 6.0 GB

Figures are weights plus KV cache and system margin at a 4,096-token context, computed from the published GGUF files. Longer contexts need more — check each model's page to size your own.

If you have a 24 GB GPU: the sweet spot

This is the tier where local coding gets genuinely good. Two standouts both fit a single 24 GB card (RTX 3090, 4090 or 5090):

KAT-Coder-V2.5-Dev — a Mixture-of-Experts model (256 experts, 8 active) that scores 69.4 on SWE-bench Verified on Kwaipilot's own card, ahead of the 35B base it was trained from. Its hybrid attention keeps the KV cache tiny, so it holds a very long context without blowing past 24 GB. Apache 2.0. It's the one to try first for agentic work.
Qwen3-Coder-30B-A3B — also MoE, fast thanks to ~3B active parameters, and it fits at Q5_K_XL (≈ 21 GB) with room to spare. A strong, well-rounded default.

Both are MoE, so remember: only a few experts run per token (that buys speed), but all experts must be resident in memory — the numbers above already reflect the full footprint.

If you have 8–16 GB: still very capable

You don't need a workstation to code locally. On a mainstream card:

Qwen2.5-Coder-7B fits an 8 GB card comfortably (≈ 6 GB at Q5) — the best small coder that runs almost anywhere.
DeepSeek-Coder-V2-Lite (16B MoE) fits 12–16 GB at Q4 (≈ 12 GB) and punches above its active size.
General 14B models like Qwen3-14B also code well and fit 12–16 GB.

If you have 32 GB: room for a dense 32B

With 32 GB you can run Qwen2.5-Coder-32B, a dense 32B that many still consider the best open coder for pure code quality, at Q3–Q4. You also get headroom to run the 24 GB models above at higher quantization (Q6–Q8) for maximum fidelity.

How to choose

Agentic workflows / long context (editing across many files): KAT-Coder or Qwen3-Coder-30B on a 24 GB card.
Best raw code quality, have 32 GB: Qwen2.5-Coder-32B.
Mainstream GPU: Qwen2.5-Coder-7B (8 GB) or DeepSeek-Coder-V2-Lite (16 GB).

New to running models? See how to run an LLM locally, and which quantization to pick. To match a model to your exact card, check what your PC can run.

The bottom line

Local coding models crossed a real threshold in 2026: a single 24 GB GPU now runs an agentic coder that scores in the high 60s on SWE-bench. Pick the largest coder that fits your VRAM at Q4 or better, and you'll have a capable assistant that never leaves your machine. Find the best fit for your GPU.

→ Check what your exact GPU can run