Best local LLM for coding on a NVIDIA GeForce RTX 4090 (2026)
All figures assume an f16 KV cache, a 0.6 GB display reserve on the GPU, and 64 GB of DDR5 system RAM for the offload tiers. Tune these in the calculator.
The verdict
Qwen3 Coder 30B A3B Q4_K_M
Fits on GPU at 32K · Coding score 9/10 · ≈ 198 tok/s
Purpose-built agentic coder. Best local fill-in-the-middle and tool-calling under 70B; useless at small talk.
llama-server -m Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf -c 32768 --flash-attn -ngl 99
Worthy alternates
Qwen3.5 27B IQ4_XS
Fits on GPU · ≈ 44 tok/s · Coding 8/10
The dense 24GB workhorse. If you want one model on a 3090 and no surprises, it's this.
Qwen3.5 35B A3B Q8_0
Expert offload · ≈ 13 tok/s · Coding 8/10
The meta pick, full stop. Near-dense-30B quality at 3B-active speed, and expert offload puts it on 8 GB cards.
Tune this for your exact RAM and settings in the calculator → · All models on the NVIDIA GeForce RTX 4090
Frequently asked questions
What is the best local LLM for coding on a NVIDIA GeForce RTX 4090?
Qwen3 Coder 30B A3B at Q4_K_M — it scores 9/10 for coding and runs as "Fits on GPU" at 32K context on the NVIDIA GeForce RTX 4090.
How much context do I need for coding?
We recommend 32K tokens for coding (minimum 16K). These picks are computed at 32K.
How fast will it run on a NVIDIA GeForce RTX 4090?
Roughly 198 tokens/sec for Qwen3 Coder 30B A3B — fast for interactive use.
Do I need more than 24 GB of VRAM for coding?
No — the pick above needs 22.2 GB of VRAM at 32K.
What settings should I use?
Start with our command: llama-server -m Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf -c 32768 --flash-attn -ngl 99 — then tune context and KV quant in the fit calculator.