作者 Jakub Rusinowski · 最后更新 2026-08-04 · 硬件数据由我们的 VRAM 引擎
The RTX 4090 (24 GB) is the best GPU for local AI coding — it runs Qwen 3.6 27B with headroom for agent contexts and an autocomplete sidecar. The used RTX 3090 delivers the same 24 GB tier for roughly half the price. On a budget, the RTX 4060 Ti 16GB runs Devstral-2 22B, and the Arc B580 is the cheapest card that runs a real coding model. Buy VRAM first, compute second.
One principle sorts the entire GPU market for coding workloads: VRAM capacity beats compute speed. A coding assistant is memory-bound twice over — the model must fit, and agent-length contexts (16–32K tokens) grow the KV cache on top. A faster card that fits a smaller model loses to a slower card that fits a better one, every time.
That principle plus the coding-model VRAM tiers produces a short list. Below, each pick is matched to the models it actually runs — figures from our compute engine, and you can verify any card + model pair in the compatibility checker.
The 24 GB tier is where local coding gets genuinely good, and the 4090 is its definitive card: Qwen 3.6 27B (~18 GB at Q4) with room for 32K contexts and a StarCoder2 3B sidecar, at 50+ tokens/sec. It also fine-tunes small models and holds resale value. If the budget reaches, stop reading here.
Same 24 GB, same model list, roughly half the street price used. Slower tokens than the 4090 (still comfortably past reading speed for a 27B at Q4) and higher power draw — the classic trade. Buying used has its own rules; see our used-GPU guide before pulling the trigger.
Sixteen gigabytes is the first genuinely agentic tier: Devstral-2 22B (~14 GB) fits, and Qwen3-Coder 8B plus autocomplete fits with room to breathe. The 4060 Ti 16GB remains the value pick; the newer 5060 Ti 16GB adds Blackwell FP4 support at a similar price point when you can find it.
The budget dark horse: 12 GB of VRAM at an entry-level price runs Qwen3-Coder 8B with context headroom, or StarCoder2 15B. Ollama and llama.cpp support Intel via Vulkan/SYCL well enough for daily use now. You give up the CUDA ecosystem — fine for inference, limiting if you later want to fine-tune.
Thirty-two gigabytes moves you past the 24 GB tier’s compromises: Qwen 2.5 Coder 32B with full context headroom, or dense-27B plus large-sidecar setups without juggling. It is the card for people who know exactly why they need it; everyone else gets 90% of the experience from a 4090 at a much lower price.
Apple Silicon is a legitimate coding-LLM platform: unified memory means a 128 GB M4 Max runs Qwen3-Coder 80B-A3B — a model no single consumer GPU fits — and MoE models play to its strengths. Count ~75% of unified memory as model-usable, and see the best Mac for local LLMs guide for configs.
Two cards (2×24 GB) unlock the 48 GB tier — Qwen3-Coder 80B-A3B all-in-VRAM — but bring PCIe-lane, PSU, and case questions with them. Worth it if you already own one 3090/4090; rarely the right first move otherwise. Our multi-GPU guide covers when it pays.
Renting before buying is the cheat code this site keeps recommending: every card above can be rented by the hour for a few dollars, with your exact models and your exact workload.
Test-drive the 24 GB tier tonight: rent a 3090/4090 by the hour, load Qwen 3.6 27B, and run your real repo through Cline before spending a grand.
完整列表见 云 AI 目录.