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Editorial ranking · 2026

Best local LLM for rtx 4070 ti

Top 7 open-source picks for rtx 4070 ti, ranked by benchmark performance and real-world fit. Updated monthly.

#1

Qwen 3 14B

14B · Alibaba · Apache 2.0

A 14B dense model from Alibaba that matches Qwen 2.5 32B Base on STEM and code, with the same hybrid thinking system as the rest of the Qwen 3 family. The pragmatic sweet spot for a single 24GB GPU.

VRAM Q4: 9 GB · Context: 128k
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#2

Phi-4 Reasoning 14B

14B · Microsoft · MIT

Microsoft's 14B reasoner that beats R1-Distill-Llama-70B on AIME and GPQA with 50x fewer parameters. MIT-licensed, English-first, with a 32K context.

VRAM Q4: 9 GB · Context: 32k
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#3

DeepSeek R1 Distill Qwen 14B

14B · DeepSeek · MIT

DeepSeek's R1 reasoning distilled into Qwen 14B under MIT. AIME24 69.7 and MATH-500 93.9 — beats o1-mini on most reasoning benchmarks.

VRAM Q4: 9 GB · Context: 128k
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#4

Phi-4 14B

14B · Microsoft · MIT

Microsoft's Phi-4 14B, trained on ultra-curated synthetic data with a heavy STEM bias. The 14B reasoning leader at the end of 2024.

VRAM Q4: 9 GB · Context: 16k
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#5

Mistral Nemo 12B Instruct

12B · Mistral AI · Apache 2.0

Mistral AI and NVIDIA's co-developed 12B instruct model with 128k context, the Tekken tokenizer, and strong European multilingual coverage.

VRAM Q4: 7 GB · Context: 125k
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#6

Gemma 3 12B

12B · Google · Gemma

The 12B sweet spot of Google's Gemma 3 line — multimodal, 128K context, and 140 languages. Fits on a single consumer GPU with room for batching.

VRAM Q4: 7 GB · Context: 125k
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#7

Qwen 2.5 14B Instruct

14B · Alibaba · Apache 2.0

Alibaba's Apache 2.0 dense 14B hitting MMLU 79.7 and HumanEval 83.5 across 29+ languages. The pragmatic sweet spot for self-hosted general-purpose chat.

VRAM Q4: 9 GB · Context: 128k
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