Conversation models that run on 64 GB

551 models with published weights that fit in 64 GB — a workstation. Counted at four-bit quantisation, weights only, leaving the machine about a third of its memory and a gigabyte for context: room for roughly 67 billion parameters. A mixture of experts is counted in full, because it is held in full even though only a few experts compute. 551 in all, a hundred to a page; this is page 6 of 6.

A workstation, or a well-specified Mac. Seventy-billion-parameter models fit at four-bit, which is the class where local output stops being obviously worse than the hosted models people pay for. Loading takes real time and the machine will be warm.

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.

• 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
• distilgpt2DistilBERT community0.1B≈0.1 GB at 4-bit
• vit-base-patch16-224Google0.1B≈0.1 GB at 4-bit
• nsfw_image_detectionFalcons.ai0.1B≈0.1 GB at 4-bit
• distilbert-base-uncasedDistilBERT community0.1B≈0.0 GB at 4-bit
• t5-smallT5 community0.1B≈0.0 GB at 4-bit
• RMBG-1.4BRIA AI0.0B≈0.0 GB at 4-bit
• detr-resnet-50AI at Meta0.0B≈0.0 GB at 4-bit
• table-transformer-structure-recognitionMicrosoft0.0B≈0.0 GB at 4-bit
• table-transformer-detectionMicrosoft0.0B≈0.0 GB at 4-bit
• segformer_b2_clothesmattmdjaga0.0B≈0.0 GB at 4-bit
• segformer-b0-finetuned-ade-512-512NVIDIA0.0B≈0.0 GB at 4-bit
• Ornith-1.0-9BOrnith0.0B≈0.0 GB at 4-bit
• AutoGLM-Phone-9BZ.ai0.0B≈0.0 GB at 4-bit

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