What is the best local LLM for coding in 2026?

Qwen3-Coder 30B is the practical pick for a 24GB-class machine, while Qwen3-Coder-Next is stronger when you have at least 64GB of usable memory. Smaller machines should use a compact Qwen model and accept a lower capability ceiling.

Can a local LLM replace Claude Code or Codex?

A strong local model can handle private code, routine edits, explanation, and many agentic tasks. Frontier cloud models still tend to be more reliable on ambiguous, long-horizon repository work, so replacement depends on your failure tolerance.

How much VRAM do I need for a local coding model?

Eight to twelve gigabytes is enough for compact assistants, 24GB is the practical sweet spot for a 30B-class quant, and 64GB opens the door to Qwen3-Coder-Next. Context length adds memory beyond the model weights.

Hardware-tier decision guide · Updated July 2026

Best Local LLM for Coding in 2026

The best model that actually fits your machine.

Cloud leaderboards ignore the first constraint of local AI: memory. These picks start with usable VRAM or unified memory, then weigh coding specialization, context, agent support, and the quality you give up by staying local.

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24GB sweet spot

Qwen3-Coder 30B

About 19GB at the official Ollama Q4 build.

64GB performance pick

Qwen3-Coder-Next

About 52GB at Q4_K_M with 256K supported context.

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Fully local runtime

Prompts, code, and tool traces stay inside your environment.

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Best local coding models by memory tier

Artifact size is not total runtime memory. Context cache, framework overhead, desktop applications, and GPU display use all need headroom.

Laptop / entry GPU8–12GB

Qwen3 8B

Fast local assistance, snippets, explanation, and small edits. Do not expect frontier-level repository work.

Use a 4-bit quant. Keep context modest so the KV cache does not consume the memory you saved on weights.

ollama run qwen3:8b

RTX 3090/4090 or larger Mac16–24GB

Qwen3-Coder 30B-A3B

The practical local-coding sweet spot: code-specialized, agentic, and small enough to fit a 24GB class setup at Q4.

The official Ollama package is about 19GB. Leave headroom for context, the runtime, and desktop applications.

ollama run qwen3-coder:30b

Mac Studio / workstation48–64GB

Qwen3-Coder-Next 80B-A3B

The strongest realistic single-workstation option here for agentic coding and large repositories.

The official Q4_K_M artifact is about 52GB. A 64GB machine is the floor; more memory gives the context cache room to breathe.

ollama run qwen3-coder-next

Multi-GPU / server250GB+

Qwen3-Coder 480B

A lab or server deployment, not a normal local recommendation. Consider a hosted open-weight API before buying hardware for it.

Ollama lists a minimum around 250GB and an artifact near 290GB. Operational complexity dominates model choice at this tier.

ollama run qwen3-coder:480b

Reality check

Can local coding models replace the frontier?

For private repositories, code explanation, tests, routine edits, and bounded refactors: often. For ambiguous tasks that require long planning, repeated recovery, and judgment across a large codebase: not consistently.

The strongest open model in the live cross-provider dataset is currently GLM-5.2 (max) at a 68.8 Coding Index. That does not mean it fits a workstation. Frontier open models can contain hundreds of billions of total parameters even when a sparse architecture activates far fewer per token.

Local selection therefore has two leaderboards: the best downloadable model and the best model your machine can run without destroying latency. This page optimizes for the second.

Open coding frontier: live benchmark shortlist

This is the capability shortlist, not the local-fit ranking. Check parameter count and quantized artifact size before downloading.

Model	Coding Index	LiveCodeBench	Terminal	Context
GLM-5.2 (max) Z AI	68.8	N/A	51%	1000K
Kimi K2.6 Kimi	61.8	N/A	44%	256K
Kimi K2.7 Code Kimi	60.8	N/A	45%	256K
MiMo-V2.5-Pro Xiaomi	60.2	N/A	43%	1000K
DeepSeek V4 Pro (Reasoning, Max Effort) DeepSeek	59.4	N/A	46%	1000K
Nex-N2-Pro Nex AGI	59.1	N/A	N/A	262K
Hy3 Tencent	58.8	N/A	N/A	256K
MiniMax-M3 MiniMax	58.6	N/A	42%	1000K

Source: Artificial Analysis via the WhatLLM live loader; fallback snapshot June 16, 2026. The highest available open LiveCodeBench score in the dataset is 90% from DeepSeek V3.2 Speciale.

GPU VRAM versus Apple unified memory

VRAM is dedicated GPU memory. Apple Silicon shares memory between CPU and GPU. A 64GB Mac does not offer all 64GB to the model, but it can load artifacts that a 24GB discrete GPU cannot. Discrete GPUs usually win on raw token throughput; large-memory Macs win on accessible capacity and simplicity.

Context size is a memory setting

A “256K context” claim is a supported maximum, not a free default. The KV cache grows with context and can turn a model that fits at 16K into one that fails at 128K. Start small, retrieve the relevant files, and measure whether larger context improves real tasks.

Sources and methodology

• Ollama Qwen3-Coder library for artifact sizes, commands, and server-tier guidance.
• Ollama Qwen3-Coder-Next library for the official 52GB Q4 artifact.
• Qwen model card for 80B total / 3B active parameters and 256K native context.
• WhatLLM live Artificial Analysis data for cross-provider coding, terminal, quality, and context metrics.

Editorially updated July 16, 2026. Hardware fit is approximate and depends on quantization, runtime, context, offloading, and concurrent applications.