← CanIRunLocal · All hardware · All models

Can I run Jais 3 0.9B locally?

IFM · 1.08B parameters · chat, reasoning.

Ollama weight files: Q4_K_M 0.67 GB (hf.co/IFM/Jais-3-0.9B-GGUF:Q4_K_M, size from the Hugging Face file listing); Q5_K_M 0.77 GB (hf.co/IFM/Jais-3-0.9B-GGUF:Q5_K_M, size from the Hugging Face file listing); Q8_0 1.15 GB (hf.co/IFM/Jais-3-0.9B-GGUF:Q8_0, size from the Hugging Face file listing); BF16 2.16 GB (hf.co/IFM/Jais-3-0.9B-GGUF:BF16, size from the Hugging Face file listing).

Yes on 85 machines in this list. The smallest is M1 8GB: BF16, about 2.8 GB, roughly 25 tokens per second, via Ollama. ollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16

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

Open Jais 3 0.9B in the checker

Hardware that fits

HardwareRuntimeQuantMemoryFitSpeedCommand
M1 8GBOllamaBF162.8 GBfits in fast memory~25 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 8GBOllamaBF162.8 GBfits in fast memory~37 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 8GBOllamaBF162.8 GBfits in fast memory~37 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 3060 8GBOllamaBF162.8 GBfits in fast memory~67 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 3060 Ti 8GBOllamaBF162.8 GBfits in fast memory~124 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 3070 8GBOllamaBF162.8 GBfits in fast memory~124 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 4060 8GBOllamaBF162.8 GBfits in fast memory~76 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 4060 Ti 8GBOllamaBF162.8 GBfits in fast memory~80 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RX 7600 8GBOllamaBF162.8 GBfits in fast memory~80 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 3080 10GBOllamaBF162.8 GBfits in fast memory~211 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M1 16GBOllamaBF162.8 GBfits in fast memory~25 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M1 Pro 16GBOllamaBF162.8 GBfits in fast memory~74 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 16GBOllamaBF162.8 GBfits in fast memory~37 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Pro 16GBOllamaBF162.8 GBfits in fast memory~74 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 16GBOllamaBF162.8 GBfits in fast memory~37 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 16GBOllamaBF162.8 GBfits in fast memory~44 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 3060 12GBOllamaBF162.8 GBfits in fast memory~100 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 3080 12GBOllamaBF162.8 GBfits in fast memory~253 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 4070 12GBOllamaBF162.8 GBfits in fast memory~140 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 5070 12GBOllamaBF162.8 GBfits in fast memory~187 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 Pro 18GBOllamaBF162.8 GBfits in fast memory~56 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 4060 Ti 16GBOllamaBF162.8 GBfits in fast memory~80 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 4070 Ti Super 16GBOllamaBF162.8 GBfits in fast memory~187 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 4080 16GBOllamaBF162.8 GBfits in fast memory~199 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 5080 16GBOllamaBF162.8 GBfits in fast memory~267 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RX 7800 XT 16GBOllamaBF162.8 GBfits in fast memory~173 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RX 9070 XT 16GBOllamaBF162.8 GBfits in fast memory~178 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 24GBOllamaBF162.8 GBfits in fast memory~37 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 24GBOllamaBF162.8 GBfits in fast memory~37 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 24GBOllamaBF162.8 GBfits in fast memory~44 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 Pro 24GBOllamaBF162.8 GBfits in fast memory~101 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 3090 24GBOllamaBF162.8 GBfits in fast memory~260 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 4090 24GBOllamaBF162.8 GBfits in fast memory~280 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RX 7900 XTX 24GBOllamaBF162.8 GBfits in fast memory~267 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M1 Max (24c GPU) 32GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M1 Max (32c GPU) 32GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M1 Pro 32GBOllamaBF162.8 GBfits in fast memory~74 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Max (30c GPU) 32GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Max (38c GPU) 32GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Pro 32GBOllamaBF162.8 GBfits in fast memory~74 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 32GBOllamaBF162.8 GBfits in fast memory~44 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 Max (30c GPU) 36GBOllamaBF162.8 GBfits in fast memory~111 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 Pro 36GBOllamaBF162.8 GBfits in fast memory~56 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 Max (32c GPU) 36GBOllamaBF162.8 GBfits in fast memory~152 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M4 Max (32c GPU) 36GBOllamaBF162.8 GBfits in fast memory~152 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Max (32c GPU) 36GBOllamaBF162.8 GBfits in fast memory~170 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
RTX 5090 32GBOllamaBF162.8 GBfits in fast memory~498 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 Max (40c GPU) 48GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 Max (40c GPU) 48GBOllamaBF162.8 GBfits in fast memory~202 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 Pro 48GBOllamaBF162.8 GBfits in fast memory~101 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M4 Max (40c GPU) 48GBOllamaBF162.8 GBfits in fast memory~202 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Max (40c GPU) 48GBOllamaBF162.8 GBfits in fast memory~227 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M1 Max (32c GPU) 64GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Max (30c GPU) 64GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Max (38c GPU) 64GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Ultra 64GBOllamaBF162.8 GBfits in fast memory~296 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 Max (40c GPU) 64GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 Max (40c GPU) 64GBOllamaBF162.8 GBfits in fast memory~202 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 Pro 64GBOllamaBF162.8 GBfits in fast memory~101 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M4 Max (40c GPU) 64GBOllamaBF162.8 GBfits in fast memory~202 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Max (40c GPU) 64GBOllamaBF162.8 GBfits in fast memory~227 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Max (38c GPU) 96GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 Max (30c GPU) 96GBOllamaBF162.8 GBfits in fast memory~111 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M3 Ultra (60c GPU) 96GBOllamaBF162.8 GBfits in fast memory~303 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M3 Ultra (80c GPU) 96GBOllamaBF162.8 GBfits in fast memory~303 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Ultra (64c GPU) 96GBOllamaBF162.8 GBfits in fast memory~444 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Ultra (80c GPU) 96GBOllamaBF162.8 GBfits in fast memory~444 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Ultra 128GBOllamaBF162.8 GBfits in fast memory~296 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M3 Max (40c GPU) 128GBOllamaBF162.8 GBfits in fast memory~148 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M4 Max (40c GPU) 128GBOllamaBF162.8 GBfits in fast memory~202 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M4 Max (40c GPU) 128GBOllamaBF162.8 GBfits in fast memory~202 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Max (40c GPU) 128GBOllamaBF162.8 GBfits in fast memory~227 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)OllamaBF162.8 GBfits in fast memory~95 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
M2 Ultra 192GBOllamaBF162.8 GBfits in fast memory~296 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
2x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF1.8 GBfits in fast memory~363 tok/sllama-server -hf IFM/Jais-3-0.9B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (60c GPU) 256GBOllamaBF162.8 GBfits in fast memory~303 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M3 Ultra (80c GPU) 256GBOllamaBF162.8 GBfits in fast memory~303 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Ultra (64c GPU) 256GBOllamaBF162.8 GBfits in fast memory~444 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
Mac Studio M5 Ultra (80c GPU) 256GBOllamaBF162.8 GBfits in fast memory~444 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
2x DGX Spark 128GBllama.cppQ8_0 GGUF1.8 GBfits in fast memory~151 tok/sllama-server -hf IFM/Jais-3-0.9B-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 GGUF1.8 GBfits in fast memory~320 tok/sllama-server -hf IFM/Jais-3-0.9B-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 GGUF1.8 GBfits in fast memory~710 tok/sllama-server -hf IFM/Jais-3-0.9B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (80c GPU) 512GBOllamaBF162.8 GBfits in fast memory~303 tok/sollama run hf.co/IFM/Jais-3-0.9B-GGUF:BF16
4x DGX Spark 128GBllama.cppQ8_0 GGUF1.8 GBfits in fast memory~134 tok/sllama-server -hf IFM/Jais-3-0.9B-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 GGUF1.8 GBfits in fast memory~626 tok/sllama-server -hf IFM/Jais-3-0.9B-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.

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