01What it can do
- 3.3B active: very fast locally
- 262k native context
- Apache 2.0 commercial
- Official Ollama tag
- —19 GB in Q4 — comfortable at 24 GB
- —Outperformed in benchmarks by GLM-4.7-Flash and Coder-Next
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Qwen3-Coder 30B-A3B?
To run Qwen3-Coder 30B-A3B locally with Q4 quantization, you need about 19 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
Affiliate links — commission possible at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.
On the go: Qwen3-Coder 30B-A3B also runs on a RTX laptop PC (24 GB of VRAM) →
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.