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Editorial ranking · 2026

Best local LLM for rtx 5060

Top 7 open-source picks for rtx 5060, ranked by benchmark performance and real-world fit. Updated monthly.

#1

Lucie 7B

7B · OpenLLM-France · Apache 2.0

A French-sovereign 7B model from OpenLLM-France, backed by CNRS and LINAGORA, with a fully transparent and auditable training corpus.

VRAM Q4: 5 GB · Context: 4k
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#2

DeepSeek R1 Distill 7B

7B · DeepSeek · MIT

A 7B DeepSeek model distilled from R1 671B with explicit chain-of-thought reasoning. Surprisingly strong on AIME and MATH for its size.

VRAM Q4: 5 GB · Context: 32k
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#3

Qwen 2.5 VL 7B

7B · Alibaba · Apache 2.0

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.

VRAM Q4: 6 GB · Context: 125k
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#4

Qwen 2.5 Omni 7B

7B · Alibaba · Apache 2.0

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.

VRAM Q4: 6 GB · Context: 32k
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#5

OLMo 3 7B

7B · Allen AI · Apache 2.0

Allen AI's fully open 7B model releasing weights, training data, and code under Apache 2.0. The reference choice for reproducible LLM research.

VRAM Q4: 5 GB · Context: 8k
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#6

Granite 4.0 H-Tiny 7B-A1B

7B · IBM · Apache 2.0

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.

VRAM Q4: 4 GB · Context: 125k
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#7

Mistral 7B Instruct

7B · Mistral AI · Apache 2.0

Mistral AI's breakout 7B instruct model. Still a go-to baseline for fast, low-cost inference and the most fine-tuned open-weight model in the wild.

VRAM Q4: 5 GB · Context: 32k
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