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Can I run Ministral 3 14B locally?

Mistral · 13.9B parameters · chat, vision.

Ollama weight files: Q4_K_M 9.1 GB (ministral-3:14b); Q8_0 15 GB (ministral-3:14b-instruct-2512-q8_0).

Yes on 59 machines in this list. The smallest is RTX 3060 Ti 8GB: Q4_K_M, about 10.7 GB, roughly 11 tokens per second, via Ollama. ollama run ministral-3:14b

Setup on DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)

Open Ministral 3 14B in the checker

Hardware that fits

HardwareRuntimeQuantMemoryFitSpeedCommand
RTX 3060 Ti 8GBOllamaQ4_K_M10.7 GBpartial offload (slower)~11 tok/sollama run ministral-3:14b
RTX 3070 8GBOllamaQ4_K_M10.7 GBpartial offload (slower)~11 tok/sollama run ministral-3:14b
RTX 4060 8GBOllamaQ4_K_M10.7 GBpartial offload (slower)~9 tok/sollama run ministral-3:14b
RX 7600 8GBOllamaQ4_K_M10.7 GBpartial offload (slower)~10 tok/sollama run ministral-3:14b
RTX 3080 10GBOllamaQ4_K_M10.7 GBpartial offload (slower)~23 tok/sollama run ministral-3:14b
M1 16GBOllamaQ4_K_M10.7 GBfits in fast memory~6 tok/sollama run ministral-3:14b
M2 16GBOllamaQ4_K_M10.7 GBfits in fast memory~9 tok/sollama run ministral-3:14b
M3 16GBOllamaQ4_K_M10.7 GBfits in fast memory~9 tok/sollama run ministral-3:14b
M4 16GBOllamaQ4_K_M10.7 GBfits in fast memory~11 tok/sollama run ministral-3:14b
RTX 3060 12GBOllamaQ4_K_M10.7 GBfits in fast memory~24 tok/sollama run ministral-3:14b
RTX 4070 12GBOllamaQ4_K_M10.7 GBfits in fast memory~33 tok/sollama run ministral-3:14b
RTX 5070 12GBOllamaQ4_K_M10.7 GBfits in fast memory~44 tok/sollama run ministral-3:14b
RTX 4060 Ti 16GBOllamaQ4_K_M10.7 GBfits in fast memory~19 tok/sollama run ministral-3:14b
RTX 4070 Ti Super 16GBOllamaQ4_K_M10.7 GBfits in fast memory~44 tok/sollama run ministral-3:14b
RTX 4080 16GBOllamaQ4_K_M10.7 GBfits in fast memory~47 tok/sollama run ministral-3:14b
RTX 5080 16GBOllamaQ4_K_M10.7 GBfits in fast memory~63 tok/sollama run ministral-3:14b
RX 7800 XT 16GBOllamaQ4_K_M10.7 GBfits in fast memory~41 tok/sollama run ministral-3:14b
RX 9070 XT 16GBOllamaQ4_K_M10.7 GBfits in fast memory~42 tok/sollama run ministral-3:14b
M2 24GBOllamaQ4_K_M10.7 GBfits in fast memory~9 tok/sollama run ministral-3:14b
M3 24GBOllamaQ4_K_M10.7 GBfits in fast memory~9 tok/sollama run ministral-3:14b
M4 24GBOllamaQ4_K_M10.7 GBfits in fast memory~11 tok/sollama run ministral-3:14b
RTX 3090 24GBOllamaQ8_016.6 GBfits in fast memory~37 tok/sollama run ministral-3:14b-instruct-2512-q8_0
RTX 4090 24GBOllamaQ8_016.6 GBfits in fast memory~40 tok/sollama run ministral-3:14b-instruct-2512-q8_0
RX 7900 XTX 24GBOllamaQ8_016.6 GBfits in fast memory~38 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M1 Pro 32GBOllamaQ8_016.6 GBfits in fast memory~11 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M2 Pro 32GBOllamaQ8_016.6 GBfits in fast memory~11 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M4 32GBOllamaQ8_016.6 GBfits in fast memory~6 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M3 Pro 36GBOllamaQ8_016.6 GBfits in fast memory~8 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M4 Max (32c GPU) 36GBOllamaQ8_016.6 GBfits in fast memory~22 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M5 Max (32c GPU) 36GBOllamaQ8_016.6 GBfits in fast memory~25 tok/sollama run ministral-3:14b-instruct-2512-q8_0
RTX 5090 32GBOllamaQ8_016.6 GBfits in fast memory~72 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M4 Pro 48GBOllamaQ8_016.6 GBfits in fast memory~15 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M4 Max (40c GPU) 48GBOllamaQ8_016.6 GBfits in fast memory~29 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M5 Max (40c GPU) 48GBOllamaQ8_016.6 GBfits in fast memory~33 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M1 Max 64GBOllamaQ8_016.6 GBfits in fast memory~21 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M2 Max 64GBOllamaQ8_016.6 GBfits in fast memory~21 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M3 Max 64GBOllamaQ8_016.6 GBfits in fast memory~21 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M4 Max 64GBOllamaQ8_016.6 GBfits in fast memory~29 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M4 Max (40c GPU) 64GBOllamaQ8_016.6 GBfits in fast memory~29 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M5 Max (40c GPU) 64GBOllamaQ8_016.6 GBfits in fast memory~33 tok/sollama run ministral-3:14b-instruct-2512-q8_0
DGX Spark 64GB (ASUS, Dell, HP, MSI, Acer, Gigabyte)OllamaQ8_016.6 GBfits in fast memory~14 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M3 Ultra 96GBOllamaQ8_016.6 GBfits in fast memory~44 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M5 Ultra 96GBOllamaQ8_016.6 GBfits in fast memory~64 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M3 Max 128GBOllamaQ8_016.6 GBfits in fast memory~21 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M4 Max 128GBOllamaQ8_016.6 GBfits in fast memory~29 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M4 Max (40c GPU) 128GBOllamaQ8_016.6 GBfits in fast memory~29 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M5 Max (40c GPU) 128GBOllamaQ8_016.6 GBfits in fast memory~33 tok/sollama run ministral-3:14b-instruct-2512-q8_0
DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)OllamaQ8_016.6 GBfits in fast memory~14 tok/sollama run ministral-3:14b-instruct-2512-q8_0
M2 Ultra 192GBOllamaQ8_016.6 GBfits in fast memory~43 tok/sollama run ministral-3:14b-instruct-2512-q8_0
2x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF16 GBfits in fast memory~29 tok/sllama-server -hf unsloth/Ministral-3-14B-Instruct-2512-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra 256GBOllamaQ8_016.6 GBfits in fast memory~44 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M5 Ultra (64c GPU) 256GBOllamaQ8_016.6 GBfits in fast memory~64 tok/sollama run ministral-3:14b-instruct-2512-q8_0
Mac Studio M5 Ultra (80c GPU) 256GBOllamaQ8_016.6 GBfits in fast memory~64 tok/sollama run ministral-3:14b-instruct-2512-q8_0
2x DGX Spark 128GBllama.cppQ8_0 GGUF16 GBfits in fast memory~12 tok/sllama-server -hf unsloth/Ministral-3-14B-Instruct-2512-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
4x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF16 GBfits in fast memory~26 tok/sllama-server -hf unsloth/Ministral-3-14B-Instruct-2512-GGUF:Q8_0 --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node
2x Mac Studio M5 Ultra (80c GPU) 256GBllama.cppQ8_0 GGUF16 GBfits in fast memory~57 tok/sllama-server -hf unsloth/Ministral-3-14B-Instruct-2512-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (80c GPU) 512GBOllamaQ8_016.6 GBfits in fast memory~44 tok/sollama run ministral-3:14b-instruct-2512-q8_0
4x DGX Spark 128GBllama.cppQ8_0 GGUF16 GBfits in fast memory~11 tok/sllama-server -hf unsloth/Ministral-3-14B-Instruct-2512-GGUF:Q8_0 --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node
4x Mac Studio M5 Ultra (80c GPU) 256GBllama.cppQ8_0 GGUF16 GBfits in fast memory~50 tok/sllama-server -hf unsloth/Ministral-3-14B-Instruct-2512-GGUF:Q8_0 --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node

Estimates, not benchmarks. Model sizes are ollama.com download sizes; memory needed adds headroom for ~8k tokens of context; speeds are rough figures from memory bandwidth. Data updated 2026-10-18.

A project by OnDevice Labs · llms.txt