Best local LLM for vision / image input on a NVIDIA GeForce RTX 4060 (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
Gemma 4 26B A4B Q6_K
Expert offload at 16K · Vision / image input score 8/10 · ≈ 14 tok/s · needs 24 GB system RAM
Google's fast MoE with native audio in. Nearly all of its weight sits in routed experts, so expert offload runs it comfortably on 12 GB cards.
llama-server -m google_gemma-4-26B-A4B-it-Q6_K.gguf -c 16384 --flash-attn -ngl 99 --n-cpu-moe 30
Worthy alternates
Qwen3.5 35B A3B Q8_0
Expert offload · ≈ 13 tok/s · Vision / image input 6/10
The meta pick, full stop. Near-dense-30B quality at 3B-active speed, and expert offload puts it on 8 GB cards.
Qwen3.5 4B Q6_K
Fits on GPU · ≈ 50 tok/s · Vision / image input 5/10
Laptop-class. Strong retrieval and extraction for its size; don't ask it to architect your codebase.
Tune this for your exact RAM and settings in the calculator → · All models on the NVIDIA GeForce RTX 4060
Frequently asked questions
What is the best local LLM for vision / image input on a NVIDIA GeForce RTX 4060?
Gemma 4 26B A4B at Q6_K — it scores 8/10 for vision / image input and runs as "Expert offload" at 16K context on the NVIDIA GeForce RTX 4060.
How much context do I need for vision / image input?
We recommend 16K tokens for vision / image input (minimum 8K). These picks are computed at 16K.
How fast will it run on a NVIDIA GeForce RTX 4060?
Roughly 14 tokens/sec for Gemma 4 26B A4B — usable for interactive use.
Do I need more than 8 GB of VRAM for vision / image input?
No — the pick above needs 7.8 GB of VRAM plus 24 GB of system RAM at 16K.
What settings should I use?
Start with our command: llama-server -m google_gemma-4-26B-A4B-it-Q6_K.gguf -c 16384 --flash-attn -ngl 99 --n-cpu-moe 30 — then tune context and KV quant in the fit calculator.