← CanIRunLocal · All hardware · All models

Can I run Qwen3.5 2B locally?

Alibaba · 2.27B parameters · chat, reasoning, coding, vision.

Ollama weight files: Q4_K_M 1.9 GB (qwen3.5:2b-q4_K_M); Q8_0 2.7 GB (qwen3.5:2b-q8_0).

Yes on 62 machines in this list. The smallest is M1 8GB: Q8_0, about 3.8 GB, roughly 20 tokens per second, via Ollama. ollama run qwen3.5:2b-q8_0

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

Open Qwen3.5 2B in the checker

Hardware that fits

HardwareRuntimeQuantMemoryFitSpeedCommand
M1 8GBOllamaQ8_03.8 GBfits in fast memory~20 tok/sollama run qwen3.5:2b-q8_0
M2 8GBOllamaQ8_03.8 GBfits in fast memory~30 tok/sollama run qwen3.5:2b-q8_0
M3 8GBOllamaQ8_03.8 GBfits in fast memory~30 tok/sollama run qwen3.5:2b-q8_0
RTX 3060 Ti 8GBOllamaQ8_03.8 GBfits in fast memory~100 tok/sollama run qwen3.5:2b-q8_0
RTX 3070 8GBOllamaQ8_03.8 GBfits in fast memory~100 tok/sollama run qwen3.5:2b-q8_0
RTX 4060 8GBOllamaQ8_03.8 GBfits in fast memory~60 tok/sollama run qwen3.5:2b-q8_0
RX 7600 8GBOllamaQ8_03.8 GBfits in fast memory~64 tok/sollama run qwen3.5:2b-q8_0
RTX 3080 10GBOllamaQ8_03.8 GBfits in fast memory~169 tok/sollama run qwen3.5:2b-q8_0
M1 16GBOllamaQ8_03.8 GBfits in fast memory~20 tok/sollama run qwen3.5:2b-q8_0
M2 16GBOllamaQ8_03.8 GBfits in fast memory~30 tok/sollama run qwen3.5:2b-q8_0
M3 16GBOllamaQ8_03.8 GBfits in fast memory~30 tok/sollama run qwen3.5:2b-q8_0
M4 16GBOllamaQ8_03.8 GBfits in fast memory~36 tok/sollama run qwen3.5:2b-q8_0
RTX 3060 12GBOllamaQ8_03.8 GBfits in fast memory~80 tok/sollama run qwen3.5:2b-q8_0
RTX 4070 12GBOllamaQ8_03.8 GBfits in fast memory~112 tok/sollama run qwen3.5:2b-q8_0
RTX 5070 12GBOllamaQ8_03.8 GBfits in fast memory~149 tok/sollama run qwen3.5:2b-q8_0
RTX 4060 Ti 16GBOllamaQ8_03.8 GBfits in fast memory~64 tok/sollama run qwen3.5:2b-q8_0
RTX 4070 Ti Super 16GBOllamaQ8_03.8 GBfits in fast memory~149 tok/sollama run qwen3.5:2b-q8_0
RTX 4080 16GBOllamaQ8_03.8 GBfits in fast memory~159 tok/sollama run qwen3.5:2b-q8_0
RTX 5080 16GBOllamaQ8_03.8 GBfits in fast memory~213 tok/sollama run qwen3.5:2b-q8_0
RX 7800 XT 16GBOllamaQ8_03.8 GBfits in fast memory~139 tok/sollama run qwen3.5:2b-q8_0
RX 9070 XT 16GBOllamaQ8_03.8 GBfits in fast memory~142 tok/sollama run qwen3.5:2b-q8_0
M2 24GBOllamaQ8_03.8 GBfits in fast memory~30 tok/sollama run qwen3.5:2b-q8_0
M3 24GBOllamaQ8_03.8 GBfits in fast memory~30 tok/sollama run qwen3.5:2b-q8_0
M4 24GBOllamaQ8_03.8 GBfits in fast memory~36 tok/sollama run qwen3.5:2b-q8_0
RTX 3090 24GBOllamaQ8_03.8 GBfits in fast memory~208 tok/sollama run qwen3.5:2b-q8_0
RTX 4090 24GBOllamaQ8_03.8 GBfits in fast memory~224 tok/sollama run qwen3.5:2b-q8_0
RX 7900 XTX 24GBOllamaQ8_03.8 GBfits in fast memory~213 tok/sollama run qwen3.5:2b-q8_0
M1 Pro 32GBOllamaQ8_03.8 GBfits in fast memory~59 tok/sollama run qwen3.5:2b-q8_0
M2 Pro 32GBOllamaQ8_03.8 GBfits in fast memory~59 tok/sollama run qwen3.5:2b-q8_0
M4 32GBOllamaQ8_03.8 GBfits in fast memory~36 tok/sollama run qwen3.5:2b-q8_0
M3 Pro 36GBOllamaQ8_03.8 GBfits in fast memory~44 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M4 Max (32c GPU) 36GBOllamaQ8_03.8 GBfits in fast memory~121 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M5 Max (32c GPU) 36GBOllamaQ8_03.8 GBfits in fast memory~136 tok/sollama run qwen3.5:2b-q8_0
RTX 5090 32GBOllamaQ8_03.8 GBfits in fast memory~398 tok/sollama run qwen3.5:2b-q8_0
M4 Pro 48GBOllamaQ8_03.8 GBfits in fast memory~81 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M4 Max (40c GPU) 48GBOllamaQ8_03.8 GBfits in fast memory~162 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M5 Max (40c GPU) 48GBOllamaQ8_03.8 GBfits in fast memory~182 tok/sollama run qwen3.5:2b-q8_0
M1 Max 64GBOllamaQ8_03.8 GBfits in fast memory~119 tok/sollama run qwen3.5:2b-q8_0
M2 Max 64GBOllamaQ8_03.8 GBfits in fast memory~119 tok/sollama run qwen3.5:2b-q8_0
M3 Max 64GBOllamaQ8_03.8 GBfits in fast memory~119 tok/sollama run qwen3.5:2b-q8_0
M4 Max 64GBOllamaQ8_03.8 GBfits in fast memory~162 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M4 Max (40c GPU) 64GBOllamaQ8_03.8 GBfits in fast memory~162 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M5 Max (40c GPU) 64GBOllamaQ8_03.8 GBfits in fast memory~182 tok/sollama run qwen3.5:2b-q8_0
DGX Spark 64GB (ASUS, Dell, HP, MSI, Acer, Gigabyte)OllamaQ8_03.8 GBfits in fast memory~76 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M3 Ultra 96GBOllamaQ8_03.8 GBfits in fast memory~243 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M5 Ultra 96GBOllamaQ8_03.8 GBfits in fast memory~356 tok/sollama run qwen3.5:2b-q8_0
M3 Max 128GBOllamaQ8_03.8 GBfits in fast memory~119 tok/sollama run qwen3.5:2b-q8_0
M4 Max 128GBOllamaQ8_03.8 GBfits in fast memory~162 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M4 Max (40c GPU) 128GBOllamaQ8_03.8 GBfits in fast memory~162 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M5 Max (40c GPU) 128GBOllamaQ8_03.8 GBfits in fast memory~182 tok/sollama run qwen3.5:2b-q8_0
DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)OllamaQ8_03.8 GBfits in fast memory~76 tok/sollama run qwen3.5:2b-q8_0
M2 Ultra 192GBOllamaQ8_03.8 GBfits in fast memory~237 tok/sollama run qwen3.5:2b-q8_0
2x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF3.1 GBfits in fast memory~208 tok/sllama-server -hf unsloth/Qwen3.5-2B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra 256GBOllamaQ8_03.8 GBfits in fast memory~243 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M5 Ultra (64c GPU) 256GBOllamaQ8_03.8 GBfits in fast memory~356 tok/sollama run qwen3.5:2b-q8_0
Mac Studio M5 Ultra (80c GPU) 256GBOllamaQ8_03.8 GBfits in fast memory~356 tok/sollama run qwen3.5:2b-q8_0
2x DGX Spark 128GBllama.cppQ8_0 GGUF3.1 GBfits in fast memory~87 tok/sllama-server -hf unsloth/Qwen3.5-2B-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 GGUF3.1 GBfits in fast memory~183 tok/sllama-server -hf unsloth/Qwen3.5-2B-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 GGUF3.1 GBfits in fast memory~406 tok/sllama-server -hf unsloth/Qwen3.5-2B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (80c GPU) 512GBOllamaQ8_03.8 GBfits in fast memory~243 tok/sollama run qwen3.5:2b-q8_0
4x DGX Spark 128GBllama.cppQ8_0 GGUF3.1 GBfits in fast memory~76 tok/sllama-server -hf unsloth/Qwen3.5-2B-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 GGUF3.1 GBfits in fast memory~358 tok/sllama-server -hf unsloth/Qwen3.5-2B-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