Best local LLM for the RTX 3080 (10 GB & 12 GB)
Last updated 2026-08-28 · Page updated 2026-08-31
Top 7 open-source picks for the RTX 3080, ranked by benchmark performance and real-world fit. Updated monthly.
Verdict (August 2026): Qwen 2.5 VL 7B is our current top pick for the RTX 3080 (7B 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.
The RTX 3080 shipped in two desktop variants, and the difference matters more for LLMs than for games: the original card carries 10 GB of VRAM, the later refresh 12 GB. Memory, not compute, is what decides which model and quantization you can load — so this ranking targets the 10 GB card. If you own the 12 GB variant (or an RTX 3080 Ti, which also has 12 GB), head to the RTX 3080 12 GB ranking instead: the extra 2 GB is exactly what lets 14B-class models fit at Q4_K_M.
On 10 GB, the sweet spot is the 7–9B class: every pick below needs about 6 GB at Q4_K_M, which leaves real headroom for the KV cache or a higher-fidelity quantization. Larger 12–14B models can still run with partial CPU offload, but expect a steep slowdown once layers spill out of VRAM.
Qwen 2.5 VL 7B
A 7B vision-language model from Alibaba with state-of-the-art results in its class, scoring 95.7 on DocVQA. Handles hour-long video, bounding-box grounding, and multilingual OCR.
Qwen 2.5 Omni 7B
Alibaba's first true omni-modal open model — text, image, audio, and video in, with text and speech out. A research-grade preview rather than a production-ready release.
Qwen 3.5 9B
Alibaba's next-generation dense 9B model with a 262K native context window and an improved toggleable thinking mode. Apache 2.0 licensed.
Qwen 3 VL 8B
The dense 8B entry in Qwen 3 VL, offering strong OCR and document analysis with a remarkable 256k multimodal context for its size.
Apertus 8B
The compact Swiss AI release trained on the Alps supercomputer, covering 1000+ languages including Swiss German and Romansh. Apache 2.0.
InternVL 3.5 8B
OpenGVLab's 8B vision-language model leading MMMU among open models. Built at Shanghai AI Lab and released under Apache 2.0.
Granite 4.0 H-Tiny 7B-A1B
IBM's edge-class hybrid MoE with 7B total and only 1B active parameters — Apache 2.0 licensed and built for embedded and low-cost serving.
Which hardware should you buy to run Qwen 2.5 VL 7B?
To run Qwen 2.5 VL 7B locally at Q4, you need ~6 GB of VRAM. The best value for this today is a RTX 5060 Ti 16GB (ASUS Dual OC) (16 GB VRAM, best $/GB).
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Frequently asked questions
What is the best local LLM for the RTX 3080?
Qwen 2.5 VL 7B tops this ranking — a 7B model, licensed under Apache 2.0, needing about 6 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 2.5 VL 7B?
At Q4 quantization, Qwen 2.5 VL 7B needs about 6 GB of VRAM and fits comfortably on a single 24 GB GPU.
Which of these models fit an 8 GB GPU?
At Q4 quantization, Qwen 2.5 VL 7B, Qwen 2.5 Omni 7B, Qwen 3.5 9B, Qwen 3 VL 8B, Apertus 8B and 2 more fit within 8 GB of VRAM.
Are the models on this the RTX 3080 list free for commercial use?
Licenses across this list include Apache 2.0. 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 32k to 256k tokens, depending on the model.