Memory tier - verified
Open models that fit in 256 GB
48 open-weight models of 63 in this directory fit in 256 GB of memory, sorted by total parameters, largest first.
48 models
Total params, largest first| Commercial | Ollama | |||||
|---|---|---|---|---|---|---|
GLM-4.7zai-org/GLM-4.7 | Z.ai | 358.3B | 198.6 GB4bit MLX | 203K | Commercial use: Yes | -No Ollama tag |
GLM-4.6zai-org/GLM-4.6 | Z.ai | 356.8B | 198.6 GB4bit MLX | 200K | Commercial use: Yes | -No Ollama tag |
GLM-5.3-Flashzai-org/GLM-5.3-Flash | Z.ai | 321.3B18B active | 199.7 GBUD-Q4_K_XL GGUF | 1.05M | Commercial use: Yes | -No Ollama tag |
MiMo-V2.6-Flash-RLXiaomiMiMo/MiMo-V2.6-Flash-RL | Xiaomi MiMo | 310.8B15B active | 167.4 GBMXFP4 GGUF | 1.05M | Commercial use: Yes | -No Ollama tag |
Hy3tencent/Hy3 | Tencent | 298.8B21B active | 182.3 GBQ4_K_M GGUF | 262K | Commercial use: Yes | -No Ollama tag |
Step 3.7 Flashstepfun-ai/Step-3.7-Flash | StepFun | 201.4B11B active | 111.5 GBQ4_K_S GGUF | 262K | Commercial use: Yes | -No Ollama tag |
Mistral Medium 3.5 128Bmistralai/Mistral-Medium-3.5-128B | Mistral AI | 127.7B | 74.9 GBQ4_K_M GGUF | 262K | Commercial use: Conditions | mistral-medium-3.5:128b |
Devstral 2 123B Instruct 2512mistralai/Devstral-2-123B-Instruct-2512 | Mistral AI | 125B | 70.3 GB4bit MLX | 262K | Commercial use: Conditions | devstral-2:123b |
NVIDIA Nemotron 3.5 Super VL 120B-A12Bnvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 | NVIDIA | 124.5B12B active | 71.8 GBQ4_0 GGUF | 1.05M | Commercial use: Yes | nemotron-3.5-super:120b |
Mistral Small 4 119B (2603)mistralai/Mistral-Small-4-119B-2603 | Mistral AI | 119.4B6.5B active | 67.8 GB4bit MLX | 262K | Commercial use: Yes | -No Ollama tag |
gpt-oss-120bopenai/gpt-oss-120b | OpenAI | 116.8B5.1B active | 62.3 GBMXFP4-Q4 MLX | 131K | Commercial use: Yes | gpt-oss:120b |
Cohere Command A (03-2025)CohereLabs/c4ai-command-a-03-2025 | Cohere | 111.1B | 62.5 GB4bit MLX | 262K | Commercial use: No | command-a:111b |
GLM-4.5-Airzai-org/GLM-4.5-Air | Z.ai | 110.5B12B active | 60.1 GB4bit MLX | 131K | Commercial use: Yes | -No Ollama tag |
Llama 4 Scout 17B-16E Instructmeta-llama/Llama-4-Scout-17B-16E-Instruct | Meta | 108.6B17B active | 61.1 GB4bit MLX | 10M | Commercial use: Conditions | llama4:scout |
GLM-4.6Vzai-org/GLM-4.6V | Z.ai | 107.7B | 61.9 GB4bit MLX | 128K | Commercial use: Yes | -No Ollama tag |
Qwen3-Coder-NextQwen/Qwen3-Coder-Next | Alibaba Cloud (Qwen) | 79.7B3B active | 44.8 GB4bit MLX | 262K | Commercial use: Yes | qwen3-coder-next |
NVIDIA Nemotron 3.5 Bolt 74B-A9Bnvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16 | NVIDIA | 74.9B9B active | 22.0 GB24GB (native 2-bit) GGUF | 262K | Commercial use: Yes | nemotron-3.5-bolt:74b-a9b |
AI21 Jamba2 Miniai21labs/AI21-Jamba2-Mini | AI21 Labs | 51.6B12B active | 31.2 GBQ4_K_M GGUF | 262K | Commercial use: Yes | -No Ollama tag |
Qwen3.6-35B-A3BQwen/Qwen3.6-35B-A3B | Alibaba Cloud (Qwen) | 36B3B active | 20.4 GB4bit MLX | 262K | Commercial use: Yes | qwen3.6:35b |
Cohere Aya Expanse 32BCohereLabs/aya-expanse-32b | Cohere | 32.3B | 19.8 GBQ4_K_M GGUF | 131K | Commercial use: No | aya-expanse:32b |
Ai2 Olmo 3.1 32B Thinkallenai/Olmo-3.1-32B-Think | Ai2 | 32.2B | 18.1 GB4bit MLX | 66K | Commercial use: Yes | olmo-3.1:32b-think |
NVIDIA Nemotron 3.5 Lightning 30B-A3Bnvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 | NVIDIA | 31.6B3B active | 17.8 GB4bit MLX | 1.05M | Commercial use: Yes | nemotron-3.5-lightning:30b |
Gemma 4 31Bgoogle/gemma-4-31B-it | 31.3B | 17.7 GBQ4_0 (QAT) GGUF | 262K | Commercial use: Yes | gemma4:31b | |
IBM Granite 4.2 30Bibm-granite/granite-4.2-30b | IBM | 29.3B | 16.5 GB4bit MLX | 131K | Commercial use: Yes | granite4.2:30b |
Qwen3.8-27BQwen/Qwen3.8-27B | Alibaba Cloud (Qwen) | 27.8B | 16.1 GB4bit MLX | 262K | Commercial use: Yes | qwen3.8:27b |
Gemma 4 26B A4Bgoogle/gemma-4-26B-A4B-it | 25.8B3.8B active | 14.4 GBQ4_0 (QAT) GGUF | 262K | Commercial use: Yes | gemma4:26b | |
Devstral Small 2 24B Instruct 2512mistralai/Devstral-Small-2-24B-Instruct-2512 | Mistral AI | 24B | 14.3 GBQ4_K_M GGUF | 262K | Commercial use: Yes | devstral-small-2:24b |
Liquid AI LFM2.5-24B-A2BLiquidAI/LFM2.5-24B-A2B | Liquid AI | 24B2.5B active | 13.6 GB4bit MLX | 131K | Commercial use: Conditions | lfm2.5:24b |
gpt-oss-20bopenai/gpt-oss-20b | OpenAI | 20.9B3.6B active | 11.2 GBMXFP4-Q4 MLX | 131K | Commercial use: Yes | gpt-oss:20b |
Phi-4-reasoning-vision-15Bmicrosoft/Phi-4-reasoning-vision-15B | Microsoft | 15.1B | 30.2 GBOfficial BF16 | 16K | Commercial use: Yes | -No Ollama tag |
Phi-4microsoft/phi-4 | Microsoft | 14.7B | 8.2 GB4bit MLX | 16K | Commercial use: Yes | phi4:14b |
Phi-4-reasoning-plusmicrosoft/Phi-4-reasoning-plus | Microsoft | 14.7B | 9.1 GBQ4_K_M GGUF | 33K | Commercial use: Yes | phi4-reasoning:plus |
Ministral 3 14B Instruct 2512mistralai/Ministral-3-14B-Instruct-2512 | Mistral AI | 13.9B | 8.2 GBQ4_K_M GGUF | 262K | Commercial use: Yes | ministral-3:14b |
Gemma 4 12Bgoogle/gemma-4-12B-it | 12B | 6.7 GB4bit MLX | 262K | Commercial use: Yes | gemma4:12b | |
Qwen3.5-9BQwen/Qwen3.5-9B | Alibaba Cloud (Qwen) | 9.7B | 5.7 GBQ4_K_M GGUF | 262K | Commercial use: Yes | qwen3.5:9b |
Ministral 3 8B Instruct 2512mistralai/Ministral-3-8B-Instruct-2512 | Mistral AI | 8.9B | 5.2 GBQ4_K_M GGUF | 262K | Commercial use: Yes | ministral-3:8b |
IBM Granite 4.2 8Bibm-granite/granite-4.2-8b | IBM | 8.8B | 4.9 GB4bit MLX | 131K | Commercial use: Yes | granite4.2:8b |
Cohere Command R7B (12-2024)CohereLabs/c4ai-command-r7b-12-2024 | Cohere | 8B | 4.5 GB4bit MLX | 131K | Commercial use: No | command-r7b:7b |
Gemma 4 E4Bgoogle/gemma-4-E4B-it | 8B | 5.0 GBQ4_K_M GGUF | 131K | Commercial use: Yes | gemma4:e4b | |
Ai2 Olmo Hybrid Instruct DPO 7Ballenai/Olmo-Hybrid-Instruct-DPO-7B | Ai2 | 7.4B | 14.9 GBOfficial BF16 | 33K | Commercial use: Yes | -No Ollama tag |
Gemma 4 E2Bgoogle/gemma-4-E2B-it | 5.1B | 3.1 GBQ4_K_M GGUF | 131K | Commercial use: Yes | gemma4:e2b | |
Qwen3.5-4BQwen/Qwen3.5-4B | Alibaba Cloud (Qwen) | 4.7B | 2.7 GBQ4_K_M GGUF | 262K | Commercial use: Yes | qwen3.5:4b |
Ministral 3 3B Instruct 2512mistralai/Ministral-3-3B-Instruct-2512 | Mistral AI | 3.8B | 2.1 GBQ4_K_M GGUF | 262K | Commercial use: Yes | ministral-3:3b |
Phi-4-mini-instructmicrosoft/Phi-4-mini-instruct | Microsoft | 3.8B | 2.2 GB4bit MLX | 131K | Commercial use: Yes | phi4-mini:3.8b |
Phi-4-mini-reasoningmicrosoft/Phi-4-mini-reasoning | Microsoft | 3.8B | 2.2 GB4bit MLX | 131K | Commercial use: Yes | phi4-mini-reasoning:3.8b |
Liquid AI LFM2.5-2.6BLiquidAI/LFM2.5-2.6B | Liquid AI | 2.7B | 1.6 GB4bit MLX | 131K | Commercial use: Conditions | -No Ollama tag |
Qwen3.5-2BQwen/Qwen3.5-2B | Alibaba Cloud (Qwen) | 2.3B | 1.3 GBQ4_K_M GGUF | 262K | Commercial use: Yes | qwen3.5:2b |
Qwen3.5-0.8BQwen/Qwen3.5-0.8B | Alibaba Cloud (Qwen) | 0.9B | 0.5 GBQ4_K_M GGUF | 262K | Commercial use: Yes | qwen3.5:0.8b |
The rest need more than 256 GB. Browse every local model.
FAQ
How many open models fit in 256 GB?
48 of 63 models in this directory have a listed download of 217.6 GB or less, 85% of 256 GB.
What is the largest model that fits in 256 GB?
By total parameters, GLM-4.7 (358.3B), whose smallest listed download is 198.6 GB, the 4bit MLX build from mlx-community/GLM-4.7-4bit. Largest is a size, not a quality ranking.
Which of these can I use commercially?
40 of the 48 ship under licenses that allow commercial use outright. The license column shows the rest: some allow it with conditions and some do not.
Why 85% of 256 GB?
The download is only the weights. Running a model also needs memory for the context (the KV cache grows with every token you keep) and for the operating system and other apps, so this site leaves 15% free as a rule of thumb. A short context needs less; a long one can need more.
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