The Best Local Coding Models for Any Setup

How to choose the best model to run locally with Atomic Chat and Kilo Code

Interest in local AI models for coding has been peaking. Modern local models are now so capable that with just 16 GB of VRAM or unified memory you can run a model that performs at a level comparable to closed-source flagships from only a few years ago. At the same time, local AI offers advantages such as offline access, privacy improvements, and greater customization. This has created a massive ecosystem — Hugging Face alone now hosts more than 135,000 GGUF-formatted models optimized for running on local devices.

But with so many options available, how do you choose the best local model for coding?

We’ve rounded up the best local coding models of 2026 by parameter size, so you can quickly find one that fits your hardware. Alongside popular choices, we’ve included some underrated local LLMs that punch far above their weight.

TL;DR: After comparing nine coding models, our recommendation is as follows. The best local LLM for coding if you have 24GB of VRAM or unified memory is Qwen 3.6 27B. If you have 12–16GB, the best local model for you is Google’s Gemma 4 12B at Q4. Finally, with 48GB of VRAM or a 64GB Mac, you can run Qwen3-Coder-Next — an almost flagship-tier model.

What are the best local coding models?

We selected the following 9 models after evaluating their specifications, overall performance, and feedback from the wider community. You can run all of the models highlighted in this post using Kilo Code as your coding harness and Atomic Chat as your inference engine.

All of these are open-weight models you can run offline, as of July 2026.

Best Local Coding Models Under 15B Parameters

Models under 15B are best for fast autocomplete, code explanation, commit messages, and single-function generation. All of them run comfortably through Kilo, Atomic Chat, Ollama or LM Studio on hardware you probably already own.

Qwen 3.5 9B

Qwen 3.5 9B has very strong reasoning abilities for this class, but limited raw knowledge. When given ample context, it can reason at the level of a 30B model. However, if you ask it to write a Svelte front end for memory (as an example), its limitations become apparent.

Runs on:

• PC: RTX 5060 (8GB) at up to ~32K context, RTX 5070 (12GB) at up to ~128K context, or RTX 5060 Ti 16GB at 262K

• Mac: 16GB unified memory

Gemma 4 12B

Gemma 4 12B is worth considering for tool-calling workflows, which it supports natively. It may not lead in coding benchmarks compared with Qwen models of similar size, but its output format reliability is a meaningful advantage for agents. Its attention is interleaved — 40 sliding-window layers that cap their cache at 1024 tokens, and 8 global layers that don’t — so context stays cheap until you push toward the full window.

Runs on:

• PC: RTX 5070 (12GB) at up to ~64K context, RTX 5060 Ti 16GB at up to ~128K context, or a 24GB card at 256K

• Mac: 16GB unified memory at up to ~128K context — the full 256K needs 32GB

Falcon H1R 7B

Falcon H1R 7B pairs Mamba2 with attention, and at 4.6GB at Q4 it's one of the few models here that genuinely fits an 8GB GPU. Note that its hybrid runs attention and Mamba2 in parallel within each block rather than alternating them, so every layer still holds a KV cache — long context costs about as much as it would on a dense model, and the full 256K needs 16GB. It's strong at reasoning and math — it outscores Qwen3-32B on math aggregate — but the Terminal-Bench Hard score marks the limit: this is a reasoning model, not an agentic one, and TII publishes no SWE-bench score at all. Its license permits commercial use but isn't OSI-approved, so read the terms if you're shipping.

Runs on:

• PC: RTX 5060 (8GB) at up to ~32K context, RTX 5070 (12GB) at up to ~128K context, or RTX 5060 Ti 16GB at 256K

• Mac: 16GB unified memory

Best Local Coding Models Between 20B and 40B Parameters

Modern 20–40B parameter models deliver excellent coding performance and can run on a single 24GB GPU. For local programming with an LLM, this range represents the sweet spot between capability and hardware requirements. Here are the models we recommend.

Note: the VRAM figures below assume Q4. If you’re using these models for long-horizon agentic coding, tool-call formatting may degrade at Q4. Then you might want to try running them at Q6 instead.

Qwen 3.6 27B

Qwen 3.6 27B is probably the best coding model that can run on a single RTX 3090 card. It can serve as a practical alternative to a hosted API or be sufficient for real offline work. Qwen recommends setting a lower temperature for more precise code generation.

Runs on:

• PC: RTX 3090, 4090 or 5090 (24GB) at up to ~64K context, or an RTX 5090 (32GB) at 262K

• Mac: 32GB unified memory at up to ~128K context — the full 262K needs 48GB

Devstral Small 2

Mistral’s Devstral Small 2 was made for structured workflows involving instructions and tool use. It has also attracted interest from users working with scientific Python, and its Apache 2.0 license allows commercial use without additional restrictions. One caveat before you plan around its context window: this is a fully dense model, so every one of its 40 layers holds a KV cache.

Context costs more here than in any other model on this list — the weights are 15GB, but the cache alone is 40GB at 262K. But sometimes you need that context. A 24GB card runs it comfortably for everyday work at 32K, and the long-context numbers are only reachable with a Q8 KV cache or datacenter hardware. Hitting 68.0% on SWE-bench Verified, Devstral Small 2 delivers great bang for the buck.

Runs on:

• PC: RTX 3090, 4090 or 5090 (24GB) at up to ~32K context, or an RTX 5090 (32GB) at up to ~64K — the full context needs far more VRAM than any single consumer card offers

• Mac: 32GB unified memory at up to ~64K context — longer windows need 64GB

Qwen 3.6 35B-A3B

Another interesting Qwen model, this one supports offload — because so few parameters are active at once, you can push the experts into system RAM and run on a 12–16GB card with 32GB of RAM. The offloaded experts are reached over PCIe, which is slower than on-card memory. Note also that its performance is inferior to that of the 27B dense model.

Runs on:

• PC: RTX 3090, 4090 or 5090 (24GB) at up to ~128K context, or an RTX 5070 (12GB) with 32GB of system RAM and expert offload

• Mac: 32GB unified memory

Cohere North Mini Code 1.0

Developed by Cohere, North Mini Code is their first local LLM designed for agentic coding workflows. Its MoE architecture uses 3 billion active parameters out of 30 billion total, allowing it to target coding tasks with a smaller inference footprint than dense models of similar scale. It competes closely with Qwen 3.6 35B-A3B and is the strongest non-Qwen option here for coding agents and repository work. There's been some feedback about potential verbosity that can burn through tokens — something to look out for.

Runs on:

• PC: RTX 3090, 4090 or 5090 (24GB) at up to ~128K context — the full 256K needs 32GB, or Q8 KV cache to stay on a 24GB card

• Mac: 32GB unified memory

NVIDIA Nemotron Cascade 2 30B-A3B

NVIDIA models are very popular in Kilo. Nemotron Cascade 2 is the long-context specialist here. Only 6 of its 52 layers use full attention — the rest are Mamba-2 and MoE — so the KV cache costs just 1.5GB at 262K, against 8GB for Qwen 3.5 9B and 40GB for Devstral Small 2. Context is essentially free on this model, and a 24GB card holds the full window. Community testing reports around 187 tokens per second on an RTX 3090 at IQ4_XS with speed staying flat out to 625K.

Runs on:

• PC: RTX 3090, 4090 or 5090 (24GB) at any context up to 262K — the KV cache adds only 1.5GB

Best Local Coding Models Between 40B and 130B Parameters

Coding models with between 40 billion and 130 billion parameters are designed for serious work within local deployments and require serious hardware to run — at least two used 3090s.

Qwen3-Coder-Next

Developed for long-context coding workflows, Qwen3-Coder-Next is designed around agent use. It was trained to integrate with Kilo, Claude Code, Qwen Code, Qoder, Trae, and Cline. The model uses reinforcement learning across different scaffold environments, which is intended to improve reliability during multi-step tool interactions.

Runs on:

• PC: two RTX 3090s (48GB) at IQ4_XS at up to ~128K context, or two RTX 5090s (64GB) at 262K

• Mac: 64GB unified memory at Q4_K_M (48.5GB weights leaves little headroom — IQ4_XS is the safer choice)

The Bottom Line

We’ve looked at nine local coding models across three size classes, from a 7B that fits an 8GB card to an 80B that wants two 3090s. Every one is an open-weight model you can download from Hugging Face and run offline through Ollama, LM Studio, or llama.cpp. Here are the key takeaways on the best local LLM for coding at each hardware tier:

• Falcon H1R 7B is the best local LLM if you have an 8GB GPU or a 16GB Mac — as long as you want reasoning and math, not an agent.

• Gemma 4 12B is the best local LLM if you have 12–16GB of VRAM.

• Qwen 3.6 27B is the best local LLM if you have a 24GB GPU or a 32GB Mac — and it’s the best local coding model of 2026 overall.

• Qwen3-Coder-Next is the best local LLM if you have 48GB of VRAM or a 64GB Mac, and it’s the one explicitly trained to run inside Kilo.

This is a guest post by Atomic Chat, a Local AI app that makes it easy to run any open-weight model from Hugging Face — try it free here.