Best local AI models for coding in 2026 (by how much RAM you have)
The best local coding LLMs in 2026 — Qwen3 Coder, GLM-4.7 Flash, Gemma 4 and Kimi K2.6 — mapped to how much RAM you actually have. Private, offline, no subscription.
The best local AI model for coding in 2026 is Qwen3 Coder for most people, with GLM-4.7 Flash the new efficiency standout — a 30B mixture-of-experts model that runs on a single 24 GB GPU and scores 59.2% on SWE-bench Verified. Kimi K2.6 tops the benchmarks outright but needs a workstation, and Gemma 4 is the safe Apache-licensed pick. The 2024-era favorites (Qwen 2.5 Coder, DeepSeek Coder) still work, but they’re no longer the answer.
The catch: “best” depends entirely on your hardware. A model that needs 24 GB of VRAM is useless advice if you have a 16 GB laptop. This guide maps the current top coding models to real memory tiers, explains the one setting that decides whether they fit, and points you at a tool that checks your exact device.
Key Takeaways
- Qwen3 Coder is the strongest local coding family in 2026 — it scales from small laptops up to workstation MoE models.
- GLM-4.7 Flash (30B total, 3B active) is the efficiency winner: single 24 GB GPU, 59.2% SWE-bench Verified, 128K context.
- RAM or VRAM is the deciding factor, not the chip — it sets which models you can load at all.
- 8–16 GB → Qwen 3.5 9B / Gemma 4 E4B. 24 GB GPU or 32 GB Mac → GLM-4.7 Flash / Qwen3 Coder Next. Workstation → Kimi K2.6.
- You still need a runner (Ollama, LM Studio) and a coding assistant (aider, Cline, Continue) to actually write code with these.
Why local coding models are worth the trouble
Running a coding model on your own machine means no subscription, no rate limits, and no code leaving your laptop. For anyone on private repos or under an NDA, that last point isn’t a nice-to-have — it’s the whole reason.
Consider Priya, a contractor who spent early 2026 paying for a cloud coding assistant. When a client’s security review banned third-party AI tools that transmit source code, she almost lost the contract. Instead she pointed aider at a local model on her workstation. Same workflow, zero code leaving the device, contract saved.
That’s the pattern: local models crossed the “genuinely useful” line in 2025, and in 2026 the open-weight models rival the proprietary labs. The only real question left is fit.
The short version: best model by RAM tier
Here’s the whole guide in one table. Find your memory, get your model — every one of these is in the onmydevice.ai model catalog.
| Your RAM / VRAM | Recommended model | Why |
|---|---|---|
| 8–16 GB | Qwen 3.5 9B or Gemma 4 E4B | Strong current-gen coding at a size that fits |
| 24 GB GPU / 32 GB Mac | GLM-4.7 Flash or Qwen3 Coder Next | Frontier-class local coding, efficient MoE |
| Multi-GPU workstation | Kimi K2.6 | Best raw benchmarks if you have the hardware |
Want the version calculated for your specific machine? The onmydevice.ai detector reads your hardware and labels each model Runs great / Runs well / Tight fit / Too heavy — no guessing, nothing typed in.
GLM-4.7 Flash: the efficiency breakthrough
The most interesting model for local coders in 2026 isn’t the biggest — it’s GLM-4.7 Flash from Zhipu AI. It’s a 30B mixture-of-experts model with only ~3B active parameters, which means it delivers big-model quality while behaving like a small one at inference time.
The numbers are the story: 59.2% on SWE-bench Verified, well ahead of older 30B-class models, at 60–220 tokens/sec on a single 24 GB consumer GPU. It has a 128K context window, so it can hold real repositories in view. If you have a 24 GB GPU or a 32 GB Mac, this is the model to try first.
MoE explained simply: a mixture-of-experts model has many “expert” sub-networks but only activates a few per token. So GLM-4.7 Flash stores 30B parameters but only computes with ~3B at a time — you get the knowledge of a large model at the speed of a small one.
Qwen3 Coder: the strongest all-round family
If GLM is the standout, Qwen3 Coder is the safe default. Alibaba’s Qwen3 Coder is the strongest coding family available locally in 2026 — it handles instruction following, fill-in-the-middle completion, multi-language generation, and repository-level reasoning well at every size.
The reason to default to it: it scales. Need something for a lighter machine? Drop to Qwen 3.5 9B, which still codes well and fits a 16 GB device. Have a workstation? Qwen3 Coder Next is an 80B MoE (3B active) built for agentic, long-horizon coding with a 256K context. Same training recipe, sized to your hardware — and Qwen3 ships under Apache 2.0, which matters if you’re building something commercial.
New to running models locally? The 5-minute local LLM walkthrough gets you from zero to a running model, then come back and swap in a coding-specific one.
The heavyweights and the safe pick
A few more models are worth knowing:
- Kimi K2.6 — Moonshot’s 1T-parameter MoE (32B active) is the best overall coding model on raw benchmarks (~58.6 SWE-Bench Pro), but it needs a multi-GPU workstation. See where it lands on your setup: Kimi K2.6.
- DeepSeek V4 Flash — the locally-sized 13B member of the DeepSeek V4 line. (The headline ~80.6% SWE-bench Verified belongs to the much larger V4 Pro, which isn’t a single-device model — don’t confuse the two.)
- Gemma 4 — Google’s family (E2B up to 31B) is the safe, Apache-licensed generalist that also codes well. Gemma 4 31B is a strong pick if you want one model for everything.
Per our own model guidance, lead with these current-gen families — Qwen3, GLM-4.7, Gemma 4 — rather than the 2024 Qwen 2.5 Coder / DeepSeek Coder generation, which still work but have been surpassed.
How much RAM do you really need? (the part other guides skip)
Most listicles say “you need 24 GB of VRAM” and stop. That’s where they fail you, because how your memory works matters as much as how much you have.
Three things decide fit:
- Unified memory vs VRAM. Apple Silicon Macs share one fast pool between CPU and GPU, so a 32 GB Mac can dedicate most of it to a model. A Windows/Linux desktop budgets against the GPU’s VRAM, not system RAM — a 32 GB PC with an 8 GB GPU is an 8 GB machine for inference. This is why the best Mac for local AI is such a common starting point.
- Disk size isn’t memory use. You need headroom for context and overhead, which is why fit budgets sit below 100% of physical RAM.
- Quantization (below) changes the size dramatically.
For a full breakdown by memory bucket, see the best models for 16 GB RAM, or jump to the live lists: 8 GB, 16 GB, 32 GB.
Quantization: the setting that decides everything
Quantization shrinks a model by storing its weights at lower precision. Q8 is near-original quality; Q4 is about half the size with a small quality hit; lower (Q3, Q2) keeps shrinking but quality drops.
Here’s what coders specifically need to know: code is less forgiving of aggressive quantization than chat. A slightly-off word in a chat reply is fine; a slightly-off token in a function signature is a bug. For coding, prefer Q5 or higher when you can afford it, and treat Q4 as the practical floor.
A concrete example: Marcus, on a 24 GB GPU, tried GLM-4.7 Flash at Q8 (~32 GB) and it spilled to system RAM and crawled. He switched to Q5 (~20 GB), which fit cleanly in VRAM and ran at 100+ tokens/sec. Same afternoon, from unusable to genuinely fast, just by matching the quant to the hardware.
Match a model to your device
This is where a generic guide runs out of road. Browsers can’t read your desktop GPU or its VRAM, so any article giving one-size-fits-all advice is guessing.
The onmydevice.ai detector does the math: it identifies your device (or lets you pick it from a searchable list grouped by Apple, NVIDIA, AMD, Intel, Qualcomm, Google), then scores every model against your actual memory budget and shows the honest label — including Too heavy, which most guides won’t tell you. Check your device now and see which coding models run well → it takes about ten seconds and nothing you type leaves the page.
How to actually code with a local model
A model is just the engine. You need two more pieces:
- A runner to serve the model: Ollama for the terminal, or LM Studio for a polished UI. Not sure which? See Ollama vs LM Studio vs Jan.
- A coding assistant that plugs into your editor or terminal:
Point any of these at your local Ollama endpoint and you have a private coding assistant with no monthly bill. Browse the full set on the apps page.
Conclusion
The best local coding model in 2026 is Qwen3 Coder for most people, with GLM-4.7 Flash the efficiency standout for anyone with a 24 GB GPU or 32 GB Mac, Kimi K2.6 for workstation owners, and Gemma 4 as the safe Apache-licensed generalist. The model name matters less than the fit: pick the size your memory can hold, keep quantization at Q5+ for code, and pair it with a runner and a coding assistant.
Your next step: check your device on onmydevice.ai to see exactly which coding models run well on your machine — private, offline, and free. Then grab a runner from the apps page and start coding with an assistant that never sends your code anywhere.
Sources: Zhipu AI releases GLM-4.7-Flash 30B-A3B (MarkTechPost), Best Open-Source & Open-Weight Coding Models 2026 (Kilo), Best Local LLMs for Coding: Kimi K2.6, Qwen, Devstral (PromptQuorum).