the best GPU for running an AI coding assistant locally in 2026 — private Copilot, your hardware

What's the best GPU for running an AI coding assistant locally?

A 24GB card: a used RTX 3090 or an RTX 4090. The coding model most people want to run, Qwen3-Coder 30B-A3B, needs about 21.0GiB at Q4_K_M with 32K tokens of context on our VRAM calculator, and 32K is roughly what it takes to keep several source files in view. A local coding assistant is a private, offline, unlimited Copilot that never sends your code anywhere, but VRAM is the gate. On a 16GB card you get a strong 14B coder; on 12GB, smaller models for autocomplete and quick help. Here's what each tier runs, on our numbers.

Why coding needs more VRAM

Two things make local coding assistants VRAM-hungry. First, the best coding models are big: Qwen3-Coder 30B-A3B keeps about 30.5B parameters in VRAM, even though only about 3.3B do the work for each token. Second, coding uses long context: the assistant needs to see your files, and every token of context sits in the KV cache on top of the weights. Qwen3-Coder 30B-A3B keeps that cache in all 48 of its layers, so every 32K tokens adds about 3GiB. That's why 24GB is the sweet spot: it fits the 30B coder with 32K tokens of your codebase in view.

"Tight" means the model uses more than 90% of the card: it loads, but anything else on the GPU can push it over. "Comfortable" leaves at least 10% free. On a 24GB card you can also go up to Q5_K_M at 8K context (about 21.7GiB, tight), but Q4_K_M with more context is usually the better trade for code. Per-GPU detail: Qwen3-Coder 30B-A3B, Qwen2.5-Coder 14B and Qwen3-Coder-Next.

A general model is also worth a try on 24GB. Qwen3.8 27B uses linear attention in three of every four layers, so its context is cheap: about 18.5GiB at Q4_K_M with 32K context on our estimate. On a 12GB card, Qwen3.5 9B fits at Q8_0 with 32K context (about 11.3GiB, tight), which leaves more room than a 14B coder.

Rent a 24GB card for an hour first

Speed matters more for coding than for chat, because autocomplete has to keep up with your typing. Test it before you buy. At 11:20 UTC on October 28, 2026, Vast.ai listed an RTX 3090 from $0.12 an hour (spot, so the machine can be interrupted), and Runpod listed one from $0.22 an hour on-demand. Point your editor at the rented card, work through a real task, and see whether it feels quick enough.

Which should you buy?

For a genuinely useful local Copilot that can reason about your code, buy a 24GB card: a used RTX 3090 is the value pick, an RTX 4090 the faster one with the same capacity (our 4090 vs 3090 comparison has the detail). That runs Qwen3-Coder 30B-A3B with 32K context, where local coding stops feeling like a compromise. If your budget caps at 16GB, an RTX 4070 Ti Super or 5070 Ti runs Qwen2.5-Coder 14B well. On 12GB (an RTX 3060 12GB), smaller models are fine for autocomplete and quick questions, not deep multi-file work. Pair the GPU with Ollama or an IDE extension that points at your local model, and you've got a private Copilot.

For a private local Copilot, buy VRAM: 24GB runs the strong coders, 16GB handles a 14B coder, 12GB does autocomplete. Pick the model to match (see Qwen3-Coder vs DeepSeek-Coder), and size it in the VRAM calculator. Need the general picture? The best GPU for local AI.