Best local LLM for rag & documents on a AMD Radeon RX 7900 XT (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
Qwen3.5 35B A3B Q8_0
Expert offload at 16K · RAG & documents score 9/10 · ≈ 13 tok/s · needs 36 GB system RAM
The meta pick, full stop. Near-dense-30B quality at 3B-active speed, and expert offload puts it on 8 GB cards.
llama-server -m Qwen3.5-35B-A3B-Q8_0.gguf -c 16384 --flash-attn -ngl 99 --n-cpu-moe 40
Worthy alternates
Qwen3.5 9B Q8_0
Fits on GPU · ≈ 55 tok/s · RAG & documents 8/10
The new small default. Frontier-distilled, natively multimodal, and embarrassingly good for 6 GB of weights.
Qwen3.5 27B IQ4_XS
Fits on GPU · ≈ 35 tok/s · RAG & documents 8/10
The dense 24GB workhorse. If you want one model on a 3090 and no surprises, it's this.
Tune this for your exact RAM and settings in the calculator → · All models on the AMD Radeon RX 7900 XT
Frequently asked questions
What is the best local LLM for rag & documents on a AMD Radeon RX 7900 XT?
Qwen3.5 35B A3B at Q8_0 — it scores 9/10 for rag & documents and runs as "Expert offload" at 16K context on the AMD Radeon RX 7900 XT.
How much context do I need for rag & documents?
We recommend 24K tokens for rag & documents (minimum 12K). These picks are computed at 16K.
How fast will it run on a AMD Radeon RX 7900 XT?
Roughly 13 tokens/sec for Qwen3.5 35B A3B — usable for interactive use.
Do I need more than 20 GB of VRAM for rag & documents?
No — the pick above needs 5.5 GB of VRAM plus 36 GB of system RAM at 16K.
What settings should I use?
Start with our command: llama-server -m Qwen3.5-35B-A3B-Q8_0.gguf -c 16384 --flash-attn -ngl 99 --n-cpu-moe 40 — then tune context and KV quant in the fit calculator.