AI models that run on 8 GB of VRAM

226 open models fit in 8 GB of VRAM at their default quant — enough for an entry GPU like an RTX 3060 / 4060 or 8 GB laptop card. Most capable first; run any of them for $0 on your own hardware.

• Fanar 1 9B Instruct — 8.8B, QCRI · ~8.0 GB VRAM
• internlm3 8b instruct — 8.8B, internlm · ~8.0 GB VRAM
• Nemotron Labs Diffusion 8B Base — 8.5B, nvidia · ~7.0 GB VRAM
• LFM2.5 8B A1B — 8.5B, LiquidAI · ~7.0 GB VRAM
• LFM2 8B A1B — 8.3B, LiquidAI · ~7.0 GB VRAM
• MiniCPM4.1 8B — 8.2B, openbmb · ~7.0 GB VRAM
• MiniCPM4 8B — 8.2B, openbmb · ~7.0 GB VRAM
• granite 3.0 8b instruct — 8.2B, ibm-granite · ~7.0 GB VRAM
• Llama 3.1 8B Instruct — 8B, Meta · ~8.0 GB VRAM
• Qwen2-VL 7B Instruct — 8B, Alibaba · ~7.0 GB VRAM
• Llama 3.1 8B Instruct (Abliterated) — 8B, mlabonne (community) · ~8.0 GB VRAM
• Hermes 3 — Llama 3.1 8B — 8B, Nous Research · ~8.0 GB VRAM
• Dolphin 3.0 — Llama 3.1 8B — 8B, Cognitive Computations · ~8.0 GB VRAM
• LLaDA 8B Instruct — 8B, GSAI-ML · ~8.0 GB VRAM
• DeepSeek R1 Distill Llama 8B — 8B, deepseek-ai · ~8.0 GB VRAM
• saiga llama3 8b — 8B, IlyaGusev · ~7.0 GB VRAM
• Meta Llama 3.1 8B Instruct — 8B, unsloth · ~8.0 GB VRAM
• LLaDA 1.5 — 8B, GSAI-ML · ~8.0 GB VRAM
• llama3.1 typhoon2 8b instruct — 8B, typhoon-ai · ~8.0 GB VRAM
• Meta Llama 3 8B Instruct — 8B, NousResearch · ~7.0 GB VRAM
• Meta Llama 3.1 8B Instruct — 8B, NousResearch · ~8.0 GB VRAM
• Llama 3.1 8B Instruct — 8B, unsloth · ~8.0 GB VRAM
• LLaDA 8B Base — 8B, GSAI-ML · ~8.0 GB VRAM
• Humanish Roleplay Llama 3.1 8B — 8B, vicgalle · ~8.0 GB VRAM
• Meta Llama 3 8B — 8B, NousResearch · ~7.0 GB VRAM
• Meta Llama 3.1 8B Instruct quantized.w4a16 — 8B, RedHatAI · ~8.0 GB VRAM
• llava onevision qwen2 7b ov — 8B, lmms-lab · ~7.0 GB VRAM
• Foundation Sec 8B Reasoning — 8B, fdtn-ai · ~8.0 GB VRAM
• Llama 3.1 Nemotron Safety Guard 8B v3 — 8B, nvidia · ~8.0 GB VRAM
• NeuralDaredevil 8B abliterated — 8B, mlabonne · ~7.0 GB VRAM
• L3 8B Stheno v3.2 — 8B, Sao10K · ~7.0 GB VRAM
• Llama 3.1 8B Lexi Uncensored V2 — 8B, Orenguteng · ~8.0 GB VRAM
• EXAONE 3.5 7.8B Instruct — 7.8B, LGAI-EXAONE · ~8.0 GB VRAM
• hf moshiko — 7.8B, kmhf · ~8.0 GB VRAM
• OpenCoder 8B Instruct — 7.8B, infly · ~7.0 GB VRAM
• internlm2 5 7b chat — 7.7B, internlm · ~8.0 GB VRAM
• internlm2 chat 7b — 7.7B, internlm · ~8.0 GB VRAM
• Qwen2 7B Instruct — 7.6B, Qwen · ~7.0 GB VRAM
• Qwen2.5 7B — 7.6B, Qwen · ~7.0 GB VRAM
• DeepSeek R1 Distill Qwen 7B — 7.6B, deepseek-ai · ~7.0 GB VRAM
• Dream v0 Instruct 7B — 7.6B, Dream-org · ~7.0 GB VRAM
• Qwen2.5 Coder 7B — 7.6B, Qwen · ~7.0 GB VRAM
• Phi mini MoE instruct — 7.6B, microsoft · ~7.0 GB VRAM
• Qwen2.5 Math 7B Instruct — 7.6B, Qwen · ~6.0 GB VRAM
• Qwen2 7B — 7.6B, Qwen · ~7.0 GB VRAM
• Qwen2.5 7B Instruct — 7.6B, unsloth · ~7.0 GB VRAM
• Qwen2.5 Math 7B — 7.6B, Qwen · ~6.0 GB VRAM
• VulnLLM R 7B — 7.6B, Virtue-AI-HUB · ~7.0 GB VRAM
• DeepHat V1 7B — 7.6B, DeepHat · ~7.0 GB VRAM
• CodeQwen1.5 7B Chat — 7.3B, Qwen · ~7.0 GB VRAM
• OLMo 2 1124 7B Instruct — 7.3B, allenai · ~8.0 GB VRAM
• Mistral 7B Instruct v0.3 — 7.2B, Mistral AI · ~8.0 GB VRAM
• Mistral 7B Instruct v0.2 — 7.2B, mistralai · ~8.0 GB VRAM
• Mistral 7B v0.1 — 7.2B, mistralai · ~8.0 GB VRAM
• Mistral 7B Instruct v0.1 — 7.2B, mistralai · ~8.0 GB VRAM
• zephyr 7b beta — 7.2B, HuggingFaceH4 · ~8.0 GB VRAM
• starcoder2 7b — 7.2B, bigcode · ~7.0 GB VRAM
• Qwen2.5 7B Instruct — 7B, Alibaba · ~7.0 GB VRAM
• Qwen2.5-Coder 7B Instruct — 7B, Alibaba · ~7.0 GB VRAM
• Qwen2.5 7B Instruct (Abliterated) — 7B, huihui-ai (community) · ~7.0 GB VRAM
• pythia 6.9b — 7B, EleutherAI · ~7.0 GB VRAM
• deepseek coder 7b instruct v1.5 — 6.9B, deepseek-ai · ~8.0 GB VRAM
• granite 4.0 h tiny — 6.9B, ibm-granite · ~7.0 GB VRAM
• OLMoE 1B 7B 0125 Instruct — 6.9B, allenai · ~6.0 GB VRAM
• OLMoE 1B 7B 0924 — 6.9B, allenai · ~6.0 GB VRAM
• llama 7b — 6.7B, huggyllama · ~7.0 GB VRAM
• granite 4.0 tiny preview — 6.7B, ibm-granite · ~7.0 GB VRAM
• Lucie 7B Instruct v1.1 — 6.7B, OpenLLM-France · ~8.0 GB VRAM
• Llama 2 7b hf — 6.7B, NousResearch · ~8.0 GB VRAM
• Llama 2 7b chat hf — 6.7B, NousResearch · ~8.0 GB VRAM
• Gemma 4 E4B DECKARD HERETIC NVFP4 — 6.2B, AEON-7 · ~8.0 GB VRAM
• Gemma 4 E4B it NVFP4 — 6B, bg-digitalservices · ~8.0 GB VRAM
• xflux text encoders — 4.8B, XLabs-AI · ~8.0 GB VRAM
• Qwen3 8B NVFP4 — 4.7B, nvidia · ~7.0 GB VRAM
• Qwen3.6 27B MTPLX Optimized Speed — 4.7B, Youssofal · ~7.0 GB VRAM
• AFM 4.5B — 4.6B, arcee-ai · ~5.0 GB VRAM
• Agents A1 4B — 4.5B, InternScience · ~6.0 GB VRAM
• Qwen3Guard Gen 4B — 4.4B, Qwen · ~6.0 GB VRAM
• Josiefied Qwen3 VL 4B Instruct abliterated beta v1 — 4.4B, Goekdeniz-Guelmez · ~6.0 GB VRAM
• Nanbeige4.2 3B — 4.2B, Nanbeige · ~5.0 GB VRAM
• Qwen3 4B — 4B, Qwen · ~5.0 GB VRAM
• Qwen3 4B Instruct 2507 — 4B, Qwen · ~5.0 GB VRAM
• Rio 3.0 Open Mini — 4B, prefeitura-rio · ~5.0 GB VRAM
• NVIDIA Nemotron 3 Nano 4B BF16 — 4B, nvidia · ~6.0 GB VRAM
• Qwen3 4B Base — 4B, Qwen · ~5.0 GB VRAM
• Qwen3 4B Thinking 2507 — 4B, Qwen · ~5.0 GB VRAM
• typhoon2.5 qwen3 4b — 4B, typhoon-ai · ~5.0 GB VRAM
• qwen3 4b heretic — 4B, DreamFast · ~5.0 GB VRAM
• Nanbeige4.1 3B — 3.9B, Nanbeige · ~5.0 GB VRAM
• Phi-3.5-mini Instruct — 3.8B, Microsoft · ~5.0 GB VRAM

Our shortlist: Best LLMs for 8 GB VRAM →

Other GPU budgets

16 GB of VRAM · 24 GB of VRAM · 48 GB of VRAM · All models

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