← CanIRunLocal · All hardware · All models

Can I run Jais 3 3.7B locally?

IFM · 5.06B parameters · chat, reasoning.

Ollama weight files: Q4_K_M 3.16 GB (hf.co/IFM/Jais-3-3.7B-GGUF:Q4_K_M, size from the Hugging Face file listing); Q5_K_M 3.64 GB (hf.co/IFM/Jais-3-3.7B-GGUF:Q5_K_M, size from the Hugging Face file listing); Q8_0 5.39 GB (hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0, size from the Hugging Face file listing); BF16 10.13 GB (hf.co/IFM/Jais-3-3.7B-GGUF:BF16, size from the Hugging Face file listing).

Yes on 85 machines in this list. The smallest is M1 8GB: Q5_K_M, about 4.5 GB, roughly 15 tokens per second, via Ollama. ollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q5_K_M

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

Open Jais 3 3.7B in the checker

Hardware that fits

HardwareRuntimeQuantMemoryFitSpeedCommand
M1 8GBOllamaQ5_K_M4.5 GBfits in fast memory~15 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q5_K_M
M2 8GBOllamaQ5_K_M4.5 GBfits in fast memory~22 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q5_K_M
M3 8GBOllamaQ5_K_M4.5 GBfits in fast memory~22 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q5_K_M
RTX 3060 8GBOllamaQ8_06.2 GBfits in fast memory~27 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
RTX 3060 Ti 8GBOllamaQ8_06.2 GBfits in fast memory~50 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
RTX 3070 8GBOllamaQ8_06.2 GBfits in fast memory~50 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
RTX 4060 8GBOllamaQ8_06.2 GBfits in fast memory~30 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
RTX 4060 Ti 8GBOllamaQ8_06.2 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
RX 7600 8GBOllamaQ8_06.2 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
RTX 3080 10GBOllamaQ8_06.2 GBfits in fast memory~85 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
M1 16GBOllamaQ8_06.2 GBfits in fast memory~10 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
M1 Pro 16GBOllamaQ8_06.2 GBfits in fast memory~30 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
M2 16GBOllamaQ8_06.2 GBfits in fast memory~15 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
M2 Pro 16GBOllamaQ8_06.2 GBfits in fast memory~30 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
M3 16GBOllamaQ8_06.2 GBfits in fast memory~15 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
M4 16GBOllamaQ8_06.2 GBfits in fast memory~18 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:Q8_0
RTX 3060 12GBOllamaBF1611 GBfits in fast memory~21 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 3080 12GBOllamaBF1611 GBfits in fast memory~54 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 4070 12GBOllamaBF1611 GBfits in fast memory~30 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 5070 12GBOllamaBF1611 GBfits in fast memory~40 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 Pro 18GBOllamaBF1611 GBfits in fast memory~12 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 4060 Ti 16GBOllamaBF1611 GBfits in fast memory~17 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 4070 Ti Super 16GBOllamaBF1611 GBfits in fast memory~40 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 4080 16GBOllamaBF1611 GBfits in fast memory~42 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 5080 16GBOllamaBF1611 GBfits in fast memory~57 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RX 7800 XT 16GBOllamaBF1611 GBfits in fast memory~37 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RX 9070 XT 16GBOllamaBF1611 GBfits in fast memory~38 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 24GBOllamaBF1611 GBfits in fast memory~8 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 24GBOllamaBF1611 GBfits in fast memory~8 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 24GBOllamaBF1611 GBfits in fast memory~9 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 Pro 24GBOllamaBF1611 GBfits in fast memory~22 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 3090 24GBOllamaBF1611 GBfits in fast memory~55 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 4090 24GBOllamaBF1611 GBfits in fast memory~60 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RX 7900 XTX 24GBOllamaBF1611 GBfits in fast memory~57 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M1 Max (24c GPU) 32GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M1 Max (32c GPU) 32GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M1 Pro 32GBOllamaBF1611 GBfits in fast memory~16 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Max (30c GPU) 32GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Max (38c GPU) 32GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Pro 32GBOllamaBF1611 GBfits in fast memory~16 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 32GBOllamaBF1611 GBfits in fast memory~9 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 Max (30c GPU) 36GBOllamaBF1611 GBfits in fast memory~24 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 Pro 36GBOllamaBF1611 GBfits in fast memory~12 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 Max (32c GPU) 36GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M4 Max (32c GPU) 36GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Max (32c GPU) 36GBOllamaBF1611 GBfits in fast memory~36 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
RTX 5090 32GBOllamaBF1611 GBfits in fast memory~106 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 Max (40c GPU) 48GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 Max (40c GPU) 48GBOllamaBF1611 GBfits in fast memory~43 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 Pro 48GBOllamaBF1611 GBfits in fast memory~22 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M4 Max (40c GPU) 48GBOllamaBF1611 GBfits in fast memory~43 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Max (40c GPU) 48GBOllamaBF1611 GBfits in fast memory~48 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M1 Max (32c GPU) 64GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Max (30c GPU) 64GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Max (38c GPU) 64GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Ultra 64GBOllamaBF1611 GBfits in fast memory~63 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 Max (40c GPU) 64GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 Max (40c GPU) 64GBOllamaBF1611 GBfits in fast memory~43 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 Pro 64GBOllamaBF1611 GBfits in fast memory~22 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M4 Max (40c GPU) 64GBOllamaBF1611 GBfits in fast memory~43 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Max (40c GPU) 64GBOllamaBF1611 GBfits in fast memory~48 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Max (38c GPU) 96GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 Max (30c GPU) 96GBOllamaBF1611 GBfits in fast memory~24 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M3 Ultra (60c GPU) 96GBOllamaBF1611 GBfits in fast memory~65 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M3 Ultra (80c GPU) 96GBOllamaBF1611 GBfits in fast memory~65 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Ultra (64c GPU) 96GBOllamaBF1611 GBfits in fast memory~95 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Ultra (80c GPU) 96GBOllamaBF1611 GBfits in fast memory~95 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Ultra 128GBOllamaBF1611 GBfits in fast memory~63 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M3 Max (40c GPU) 128GBOllamaBF1611 GBfits in fast memory~32 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M4 Max (40c GPU) 128GBOllamaBF1611 GBfits in fast memory~43 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M4 Max (40c GPU) 128GBOllamaBF1611 GBfits in fast memory~43 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Max (40c GPU) 128GBOllamaBF1611 GBfits in fast memory~48 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)OllamaBF1611 GBfits in fast memory~20 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
M2 Ultra 192GBOllamaBF1611 GBfits in fast memory~63 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
2x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF6.2 GBfits in fast memory~77 tok/sllama-server -hf IFM/Jais-3-3.7B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (60c GPU) 256GBOllamaBF1611 GBfits in fast memory~65 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M3 Ultra (80c GPU) 256GBOllamaBF1611 GBfits in fast memory~65 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Ultra (64c GPU) 256GBOllamaBF1611 GBfits in fast memory~95 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
Mac Studio M5 Ultra (80c GPU) 256GBOllamaBF1611 GBfits in fast memory~95 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
2x DGX Spark 128GBllama.cppQ8_0 GGUF6.2 GBfits in fast memory~32 tok/sllama-server -hf IFM/Jais-3-3.7B-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 GGUF6.2 GBfits in fast memory~68 tok/sllama-server -hf IFM/Jais-3-3.7B-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 GGUF6.2 GBfits in fast memory~151 tok/sllama-server -hf IFM/Jais-3-3.7B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (80c GPU) 512GBOllamaBF1611 GBfits in fast memory~65 tok/sollama run hf.co/IFM/Jais-3-3.7B-GGUF:BF16
4x DGX Spark 128GBllama.cppQ8_0 GGUF6.2 GBfits in fast memory~28 tok/sllama-server -hf IFM/Jais-3-3.7B-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 GGUF6.2 GBfits in fast memory~134 tok/sllama-server -hf IFM/Jais-3-3.7B-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 2026-10-20.

A project by OnDevice Labs · llms.txt