The best open coding model in the world needs a datacenter to run. The best coding model you can run on a single 24GB card is a different name entirely — and it's shockingly close for real work.
The best open coding model in the world right now is Kimi K3, which just took first place on the Frontend Code Arena leaderboard — ahead of Claude Fable 5 and GPT-5.6 Sol. It's also completely unrunnable at home: at ~1.4TB it needs a datacenter node just to load. So let's ask the question that actually matters for a developer with a normal PC: what's the best coding model I can run on a single GPU? The answer isn't a household name, it's genuinely excellent for day-to-day work, and it fits comfortably on a 24GB card — and the reason it runs so fast is a trick worth understanding.
The MoE trick that makes a 30B model fast
Here's the mechanism that makes local coding good in 2026. Qwen3-Coder-30B-A3B is a Mixture-of-Experts model: 30 billion total parameters, but only about 3 billion active per token. When it generates code, each token only runs through a small slice of the model — so it computes at roughly the speed of a 3B model while drawing on the knowledge of a 30B one. That's the opposite of the problem that makes Kimi K3 unrunnable: MoE still needs all the parameters in memory, but at 30B total that's ~20GB — which fits a single 24GB card, where Kimi's 2.8T does not. Same architecture, wildly different practicality, entirely because of scale.
The practical upshot: on a 24GB card, a 30B MoE coder feels snappy in a way a dense 32B model doesn't, because you're paying 3B-worth of compute per token. For an interactive coding assistant — where you want completions and edits to appear quickly — that speed is the difference between a tool you reach for and one you tolerate. It's the single best reason the MoE coders have become the default local recommendation.
Does it fit? — Qwen3-Coder-30B (Q4) needs ≈20GB
Qwen3-Coder-30B (Q4) needs ≈20 GB
RTX 3060 (12GB)12 GBover 8 GB
RTX 5060 Ti (16GB)16 GBover 4 GB
RTX 3090 (24GB)24 GBfits
RTX 4090 (24GB)24 GBfits
The best local coding model needs a 24GB card. 16GB is too tight for the 30B tier; 12GB fits only smaller 14B coders.
Qwen3-Coder-30B-A3B is the all-round pick for most people — fast, capable, and the strongest jump in multi-file coherence over the 14B tier. Qwen2.5-Coder-32B, the dense model, is the one to reach for when you want maximum single-model consistency on a hard problem and don't mind waiting a little longer per token. Both need roughly the same ~20GB, so the choice is about speed-versus-thoroughness, not fit. If you're choosing a card for this, our RTX 4090 vs 3090 breakdown covers the two most sensible 24GB options — the 3090 is the value pick, the 4090 the faster one.
A 30B MoE coder on a 24GB card gives you fast, capable completions locally — no API, no data leaving your machine. · Unsplash
What about smaller cards?
If you don't have 24GB, you're not shut out — you just drop to the 14B coding tier. A 12GB card like the RTX 3060 runs a 14B coder well, which is genuinely useful for single-file work and completions, though you'll feel the gap in multi-file reasoning. 16GB (a 5060 Ti) is an awkward middle — better than 12GB but still short of comfortably running the 30B tier with context. The honest advice: if local coding is a real part of your workflow, a 24GB card is the tier worth targeting, and a used 3090 is the cheapest ticket to it.
Verdict
Qwen3-Coder-30B-A3B on a 24GB card
You can't run the leaderboard-topping Kimi K3 at home, but you don't need to. Qwen3-Coder-30B-A3B on a single 24GB GPU is fast, private and genuinely good at real coding work — its Mixture-of-Experts design means a 30B model that computes like a small one. Pair it with a used RTX 3090 for the cheapest capable setup, or a 4090 for more speed. Reach for the dense Qwen2.5-Coder-32B when you want maximum consistency on a hard problem. For most developers, that's the local coding stack in 2026.
Best for: Qwen3-Coder-30B-A3B: fast interactive coding on 24GB. Qwen2.5-Coder-32B: deep review/consistency. 14B coders: 12–16GB cards. Kimi K3: only via API/host.
The questions people actually ask
Can I run a coding model as good as Kimi K3 locally?
Not the same class — Kimi K3 is a 2.8T frontier model that needs datacenter hardware. But you don't need K3-level capability for most coding. Qwen3-Coder-30B-A3B on a 24GB card handles the large majority of real coding tasks — completions, refactors, multi-file edits — fast and privately. The gap to a frontier model shows on the hardest, most novel problems, not on day-to-day work.
Is the MoE coder actually faster than a dense 32B?
Yes, meaningfully — because only ~3B of its 30B parameters are active per token, it computes far less per token than a dense 32B model that runs all 32B every time. Both occupy similar VRAM (~20GB), but the MoE model generates noticeably faster, which matters a lot for an interactive assistant. That speed is the main reason Qwen3-Coder-30B-A3B is the default recommendation over the dense option.
Do I need 24GB, or will 16GB work for coding?
16GB runs 14B-class coding models fine, which cover a lot of everyday use. But the 30B tier — where local coding gets genuinely good at multi-file work — wants ~20GB plus context headroom, so 24GB is the target. If you're buying specifically for local coding, a 24GB card (a used 3090 is cheapest) is the tier worth stretching for; 16GB is a capable step down, not the ceiling.
Is local coding private enough for company code?
That's the main reason to run it locally — nothing leaves your machine, so there's no third-party API seeing proprietary code. Whether it satisfies your specific compliance requirements is a question for your security team, but architecturally, a fully local model is as private as it gets: no network calls, no logging by a provider, works air-gapped. For privacy-sensitive codebases, that's the whole appeal.
The headline model will always be the one you can't run — today it's Kimi K3, next month it'll be something bigger. The model you can run keeps getting good enough that the gap matters less every release. Size a coder to your GPU in our VRAM calculator, start with Qwen3-Coder-30B-A3B if you have 24GB, and read what else fits in 24GB if you want a general model alongside your coding one.
