AI models that run on 16 GB of VRAM

237 open models fit in 16 GB of VRAM at their default quant — enough for a mid-range card like an RTX 4060 Ti 16 GB / 4070 Ti Super. Most capable first; run any of them for $0 on your own hardware.

gpt oss safeguard 20b — 21.5B, openai · ~16 GB VRAM
gpt-oss-20b — 21B, OpenAI · ~15 GB VRAM
gpt oss 20b BF16 — 20.9B, unsloth · ~15 GB VRAM
NVIDIA Nemotron 3 Nano 30B A3B NVFP4 — 18.2B, nvidia · ~13 GB VRAM
Qwen3 30B A3B NVFP4 — 17.5B, RedHatAI · ~13 GB VRAM
Qwen3 32B NVFP4 — 17.2B, nvidia · ~15 GB VRAM
Param2 17B A2.4B Thinking — 17.2B, bharatgenai · ~12 GB VRAM
LLaDA2.0 mini — 16.3B, inclusionAI · ~12 GB VRAM
DeepSeek-Coder-V2-Lite Instruct — 15.7B, DeepSeek · ~11 GB VRAM
DeepSeek V2 Lite Chat — 15.7B, deepseek-ai · ~15 GB VRAM
DeepSeek V2 Lite — 15.7B, deepseek-ai · ~15 GB VRAM
Qwen2.5 Coder 14B Instruct — 14.8B, Qwen · ~14 GB VRAM
Qwen2.5 14B Instruct — 14.8B, Qwen · ~14 GB VRAM
Qwen3 14B — 14.8B, Qwen · ~13 GB VRAM
Qwen3 14B Base — 14.8B, Qwen · ~13 GB VRAM
Qwen3 14B Instruct — 14.8B, OpenPipe · ~13 GB VRAM
Qwen1.5 MoE A2.7B — 14.3B, Qwen · ~12 GB VRAM
Phi-4 — 14B, Microsoft · ~13 GB VRAM
Llama 2 13b chat hf — 13B, meta-llama · ~16 GB VRAM
HarmBench Llama 2 13b cls — 13B, cais · ~11 GB VRAM
NVIDIA Nemotron Nano 12B v2 — 12.3B, nvidia · ~15 GB VRAM
MN 12B Mag Mell R1 — 12.2B, inflatebot · ~12 GB VRAM
Gemma 3 12B — 12B, Google · ~13 GB VRAM
Gemma 4 12B OBLITERATED — 12B, OBLITERATUS · ~14 GB VRAM
Apertus 70B Instruct 2509 quantized.w4a16 — 11.3B, RedHatAI · ~14 GB VRAM
Falcon3-10B Instruct — 10B, TII · ~10 GB VRAM
Darwin 9B NEG — 9.7B, ansulev · ~12 GB VRAM
SeeClick — 9.7B, cckevinn · ~12 GB VRAM
gemma 2 9b — 9.2B, google · ~11 GB VRAM
Gemma 2 9B Instruct — 9B, Google · ~9.0 GB VRAM
NVIDIA Nemotron Nano 9B v2 — 8.9B, nvidia · ~10 GB VRAM
NVIDIA Nemotron Nano 9B v2 Japanese — 8.9B, nvidia · ~10 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
gemma 7b — 8.5B, google · ~11 GB VRAM
Qwen3-8B — 8.2B, Alibaba · ~9.0 GB VRAM
Qwen3 8B Base — 8.2B, Qwen · ~9.0 GB VRAM
DeepSeek R1 0528 Qwen3 8B — 8.2B, deepseek-ai · ~9.0 GB VRAM
Qwen3 14B NVFP4 — 8.2B, nvidia · ~9.0 GB VRAM
granite 3.1 8b instruct — 8.2B, ibm-granite · ~9.0 GB VRAM
Qwen3Guard Gen 8B — 8.2B, Qwen · ~9.0 GB VRAM
granite 3.0 8b instruct — 8.2B, ibm-granite · ~7.0 GB VRAM
granite 3.3 8b instruct — 8.2B, ibm-granite · ~9.0 GB VRAM
Apertus 8B Instruct 2509 — 8.1B, swiss-ai · ~9.0 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
Meta Llama 3 8B Instruct — 8B, meta-llama · ~10 GB VRAM
Llama 3.1 8B — 8B, meta-llama · ~10 GB VRAM
Meta Llama 3 8B — 8B, meta-llama · ~10 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
gemma 4 E4B it OBLITERATED — 8B, OBLITERATUS · ~10 GB VRAM
LLaDA 1.5 — 8B, GSAI-ML · ~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
Llama Guard 3 8B — 8B, meta-llama · ~10 GB VRAM
llava onevision qwen2 7b ov — 8B, lmms-lab · ~7.0 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
MiMo 7B Base — 7.8B, XiaomiMiMo · ~9.0 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
MiMo 7B RL — 7.8B, XiaomiMiMo · ~9.0 GB VRAM
Qwen1.5 7B — 7.7B, Qwen · ~15 GB VRAM
internlm2 5 7b chat — 7.7B, internlm · ~8.0 GB VRAM
Qwen 7B Chat — 7.7B, Qwen · ~15 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
Qwen2.5 7B Instruct — 7.6B, unsloth · ~7.0 GB VRAM
Olmo 3 7B Instruct SFT — 7.3B, allenai · ~14 GB VRAM
Olmo 3 1025 7B — 7.3B, allenai · ~14 GB VRAM
falcon mamba 7b — 7.3B, tiiuae · ~14 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

Other GPU budgets

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