Conversation models that run on 128 GB

613 models with published weights that fit in 128 GB — a large Mac or a server card. Counted at four-bit quantisation, weights only, leaving the machine about a third of its memory and a gigabyte for context: room for roughly 136 billion parameters. A mixture of experts is counted in full, because it is held in full even though only a few experts compute. 613 in all, a hundred to a page; this is page 6 of 7.

A large Mac or a server card. This is where the biggest openly published models come within reach — and where the distinction between total and active parameters starts to decide everything, because a mixture of experts computes with a fraction of itself and must still be held whole.

Models that answer in prose across whatever subject you put to them. This is the largest category and the least differentiated: most of them are competent at most things, so the choice usually comes down to price, context length and how the maker behaves about availability. Look at what a model costs per million tokens in and out — the output rate is often four or five times the input rate, and it is the one that decides your bill.

• gemma-1.1-2b-itGoogle2.0B≈1.3 GB at 4-bit
• helium-1-preview-2bKyutai2.0B≈1.3 GB at 4-bit
• Youtu-LLM-2BTencent Hunyuan2.0B≈1.3 GB at 4-bit
• moondream2vikhyatk1.9B≈1.3 GB at 4-bit
• VibeThinker-1.5BWeiboAI1.8B≈1.2 GB at 4-bit
• Qwen3.6-27B-DFlashZ Lab1.7B≈1.1 GB at 4-bit
• SmolLM-1.7BHuggingFaceTB1.7B≈1.1 GB at 4-bit
• SmolLM2-1.7BHuggingFaceTB1.7B≈1.1 GB at 4-bit
• stablelm-2-1_6bStability AI1.6B≈1.1 GB at 4-bit
• stablelm-2-zephyr-1_6bStability AI1.6B≈1.1 GB at 4-bit
• LFM2.5-VL-1.6BLiquid AI1.6B≈1.0 GB at 4-bit
• LFM2-VL-1.6BLiquid AI1.6B≈1.0 GB at 4-bit
• Qwen2.5-1.5BAlibaba1.5B≈1.0 GB at 4-bit
• Qwen2.5-1.5B-InstructAlibaba1.5B≈1.0 GB at 4-bit1 also selling it hosted
• ReaderLM-v2Jina AI1.5B≈1.0 GB at 4-bit
• reader-lm-1.5bJina AI1.5B≈1.0 GB at 4-bit
• Hymba-1.5B-InstructNVIDIA1.5B≈1.0 GB at 4-bit
• Arch-Router-1.5Bkatanemo1.5B≈1.0 GB at 4-bit
• Hymba-1.5B-BaseNVIDIA1.5B≈1.0 GB at 4-bit
• HyperCLOVAX-SEED-Text-Instruct-1.5BHyperCLOVA X1.5B≈1.0 GB at 4-bit
• Qwen2-1.5B-InstructAlibaba1.5B≈1.0 GB at 4-bit1 also selling it hosted
• starvector-1b-im2svgstarvector1.4B≈0.9 GB at 4-bit
• phi-1Microsoft1.4B≈0.9 GB at 4-bit
• phi-1_5Microsoft1.4B≈0.9 GB at 4-bit
• Ouro-1.4BByteDance1.4B≈0.9 GB at 4-bit
• MiniCPM-V-4.6OpenBMB1.3B≈0.8 GB at 4-bit
• gpt-neo-1.3BEleutherAI1.3B≈0.8 GB at 4-bit
• nllb-200-distilled-1.3Bfacebook1.3B≈0.8 GB at 4-bit
• opt-1.3bfacebook1.3B≈0.8 GB at 4-bit
• EXAONE-4.0-1.2BLGAI-EXAONE1.3B≈0.8 GB at 4-bit
• SpatialLM-Llama-1BManycore Research1.2B≈0.8 GB at 4-bit
• LFM2.5-1.2B-BaseLiquid AI1.2B≈0.8 GB at 4-bit
• LFM2.5-1.2B-JPLiquid AI1.2B≈0.8 GB at 4-bit
• HRM-Text-1BSapient AI1.2B≈0.8 GB at 4-bit
• LFM2-1.2BLiquid AI1.2B≈0.8 GB at 4-bit
• LFM2.5-1.2B-InstructLiquid AI1.2B≈0.8 GB at 4-bit
• MinerU2.5-2509-1.2BOpenDataLab1.2B≈0.8 GB at 4-bit
• TinyLlama-1.1B-Chat-v1.0TinyLlama1.1B≈0.7 GB at 4-bit
• TinyLlama-1.1B-intermediate-step-1431k-3TTinyLlama1.1B≈0.7 GB at 4-bit
• MiniCPM5-1BOpenBMB1.1B≈0.7 GB at 4-bit
• vaultgemma-1bGoogle1.0B≈0.7 GB at 4-bit
• Gemma3-1B-ITLiteRT Community (FKA TFLite)1.0B≈0.7 GB at 4-bit
• MolmoE-1B-0924Allen Institute for AI (Ai2)1.0B≈0.7 GB at 4-bit
• antares-1bfdtn-ai1.0B≈0.7 GB at 4-bit
• granite-4.0-h-1bIBM Granite1.0B≈0.7 GB at 4-bit
• gemma-3-1b-itGoogle1.0B≈0.6 GB at 4-bit
• gemma-3-1b-ptGoogle1.0B≈0.6 GB at 4-bit
• CLIP-ViT-H-14-laion2B-s32B-b79KLAION eV1.0B≈0.6 GB at 4-bit
• MobileLLM-R1-950MAI at Meta0.9B≈0.6 GB at 4-bit
• siglip-so400m-patch14-384Google0.9B≈0.6 GB at 4-bit
• bitnet-b1.58-2B-4TMicrosoft0.8B≈0.6 GB at 4-bit
• gpt2-largeOpenAI community0.8B≈0.5 GB at 4-bit
• t5gemma-2-270m-270mGoogle0.8B≈0.5 GB at 4-bit
• Florence-2-largeMicrosoft0.8B≈0.5 GB at 4-bit
• Florence-2-large-ftMicrosoft0.8B≈0.5 GB at 4-bit
• FastVLM-0.5BApple0.8B≈0.5 GB at 4-bit
• Qwen3-0.6BAlibaba0.8B≈0.5 GB at 4-bit1 also selling it hosted
• Qwen3-0.6B-BaseAlibaba0.6B≈0.4 GB at 4-bit
• bloom-560mBigScience Workshop0.6B≈0.4 GB at 4-bit
• Qwen1.5-0.5BAlibaba0.5B≈0.3 GB at 4-bit
• Qwen2-0.5BAlibaba0.5B≈0.3 GB at 4-bit
• reader-lm-0.5bJina AI0.5B≈0.3 GB at 4-bit
• Qwen2-0.5B-InstructAlibaba0.5B≈0.3 GB at 4-bit
• Qwen2.5-0.5BAlibaba0.5B≈0.3 GB at 4-bit
• Qwen2.5-0.5B-InstructAlibaba0.5B≈0.3 GB at 4-bit1 also selling it hosted
• blip-image-captioning-largeSalesforce0.5B≈0.3 GB at 4-bit
• LFM2.5-VL-450MLiquid AI0.4B≈0.3 GB at 4-bit
• PairRMLLM Blender0.4B≈0.3 GB at 4-bit
• CLIP-GmP-ViT-L-14zer0int0.4B≈0.3 GB at 4-bit
• circuit-sparsityOpenAI0.4B≈0.3 GB at 4-bit
• bart-large-cnnAI at Meta0.4B≈0.3 GB at 4-bit
• ModernBERT-largeAnswer.AI0.4B≈0.3 GB at 4-bit
• blip-vqa-baseSalesforce0.4B≈0.3 GB at 4-bit
• LFM2-350MLiquid AI0.4B≈0.2 GB at 4-bit
• LFM2.5-350MLiquid AI0.4B≈0.2 GB at 4-bit
• nougat-basefacebook0.3B≈0.2 GB at 4-bit
• bert-large-uncased-whole-word-masking-finetuned-squadBERT community0.3B≈0.2 GB at 4-bit
• mdeberta-v3-base-squad2timpal0l0.3B≈0.2 GB at 4-bit
• functiongemma-270m-itGoogle0.3B≈0.2 GB at 4-bit
• gemma-3-270mGoogle0.3B≈0.2 GB at 4-bit
• gemma-3-270m-itGoogle0.3B≈0.2 GB at 4-bit
• granite-docling-258MIBM Granite0.3B≈0.2 GB at 4-bit
• Florence-2-baseMicrosoft0.2B≈0.2 GB at 4-bit
• t5-baseT5 community0.2B≈0.1 GB at 4-bit
• chatgpt_paraphraser_on_T5_baseHumarin0.2B≈0.1 GB at 4-bit
• BiRefNetZhengPeng70.2B≈0.1 GB at 4-bit
• RMBG-2.0BRIA AI0.2B≈0.1 GB at 4-bit
• bert-base-multilingual-casedBERT community0.2B≈0.1 GB at 4-bit
• gpt-neo-125mEleutherAI0.2B≈0.1 GB at 4-bit
• ModernBERT-baseAnswer.AI0.1B≈0.1 GB at 4-bit
• MagicPrompt-Stable-DiffusionGustavosta0.1B≈0.1 GB at 4-bit
• gpt2OpenAI community0.1B≈0.1 GB at 4-bit
• distilbert-base-multilingual-caseddistilbert0.1B≈0.1 GB at 4-bit
• SmolLM2-135MHuggingFaceTB0.1B≈0.1 GB at 4-bit
• roberta-baseFacebook AI community0.1B≈0.1 GB at 4-bit
• roberta-base-squad2deepset0.1B≈0.1 GB at 4-bit
• bert-base-uncasedBERT community0.1B≈0.1 GB at 4-bit
• bert-base-casedBERT community0.1B≈0.1 GB at 4-bit
• bert-base-chineseBERT community0.1B≈0.1 GB at 4-bit
• BEN2Prama LLC0.1B≈0.1 GB at 4-bit

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