Best local LLM for radeon rx 7900 xtx
Last updated 2026-08-28 · Page updated 2026-08-31
Top 8 open-source picks for radeon rx 7900 xtx, ranked by benchmark performance and real-world fit. Updated monthly.
Verdict (August 2026): Qwen 3 30B-A3B is our current top pick for radeon rx 7900 xtx (30B params · Apache 2.0). The full ranking and per-model reasoning follow.
This ranking is built from real specs — parameter count, VRAM at 4-bit quantization, license terms, and available benchmark scores — pulled from BestLLMfor's tracked catalog of 239 models, not vendor marketing. Data reflects the catalog as of 2026-08-28; see our full methodology for how models are scored and re-ranked.
Qwen 3 30B-A3B
Alibaba's Qwen 3 MoE with 30B total and just 3B active parameters, supporting hybrid thinking mode. MMLU 81.4, AIME24 80.4, 100+ languages, Apache 2.0.
Granite 4.0 H-Small 32B-A9B
IBM's hybrid Mamba-2 + MoE model with 32B total and 9B active parameters, engineered to slash long-context memory use by roughly 70% versus comparable transformers under Apache 2.0.
Qwen 3 VL 30B-A3B
Qwen 3 VL's sweet spot: a 30B MoE with 3B active parameters and 256k context. Delivers most of the 235B's quality at a fraction of the hardware cost.
Trinity Mini 26B-A3B
Arcee AI's US-built MoE with 3B active parameters out of 26B total. Apache-licensed, fast in practice, and tuned for agent-style workloads.
Kanana 2 30B-A3B Thinking
Kakao's agentic 30B MoE (3B active) with native hybrid thinking and Korean-first training. Apache 2.0 with MLA attention and 131k context.
Qwen 3 Omni 30B-A3B
Alibaba's omni-modal 30B MoE (3B active) with streaming speech, 119-language ASR, and Apache 2.0 licensing. The most accessible truly omnimodal open model.
LLaDA 2.0 Uni 16B
Ant Group's first open Apache 2.0 diffusion LLM: a 16B/1B MoE paired with a 6.2B diffusion decoder, unifying text and vision generation and editing. Released April 2026.
DeepSeek R1 Distill 32B
The 32B DeepSeek R1 distill — the best accessible open-weight reasoner we've tested. Explicit chain-of-thought, MIT-licensed, runs on a single 24GB GPU.
Which hardware should you buy to run Qwen 3 30B-A3B?
To run Qwen 3 30B-A3B locally at Q4, you need ~19 GB of VRAM. The best value for this today is a GMKtec EVO-X2 64GB (Ryzen AI Max+ 395 mini PC) (64 GB unified memory, half the price of an RTX 5090).
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Frequently asked questions
What is the best local LLM for radeon rx 7900 xtx?
Qwen 3 30B-A3B tops this ranking — a 30B model, licensed under Apache 2.0, needing about 19 GB of VRAM at Q4 quantization. See the full list below for the runner-ups and how they compare.
How much VRAM do I need to run Qwen 3 30B-A3B?
At Q4 quantization, Qwen 3 30B-A3B needs about 19 GB of VRAM and fits comfortably on a single 24 GB GPU.
Which of these models fit an 16 GB GPU?
At Q4 quantization, Trinity Mini 26B-A3B fit within 16 GB of VRAM.
Are the models on this radeon rx 7900 xtx list free for commercial use?
Licenses across this list include Apache 2.0, MIT. Check the specific license of each model on its catalog page before deploying commercially, as terms vary by author.
What context window do these models support?
Context windows on this list range from 8k to 256k tokens, depending on the model.