← CanIRunLocal · All hardware · All models
Alibaba · 9.65B parameters · chat, reasoning, coding, vision.
Ollama weight files: Q4_K_M 6.6 GB (qwen3.5:9b-q4_K_M); Q8_0 10 GB (qwen3.5:9b-q8_0).
Yes on 59 machines in this list. The smallest is RTX 3060 Ti 8GB: Q4_K_M, about 7.1 GB, roughly 47 tokens per second, via LM Studio. lms get unsloth/Qwen3.5-9B-GGUF@Q4_K_M
ollama run qwen3.5:9b-q8_0ollama run qwen3.5:9b-q8_0ollama run qwen3.5:9b-q8_0ollama run qwen3.5:9b-q8_0lms get unsloth/Qwen3.5-9B-GGUF@Q8_0llama-server -hf unsloth/Qwen3.5-9B-GGUF:Q8_0 -c 8192vllm serve Qwen/Qwen3.5-9B --max-model-len 8192 --gpu-memory-utilization 0.9| Hardware | Runtime | Quant | Memory | Fit | Speed | Command |
|---|---|---|---|---|---|---|
| RTX 3060 Ti 8GB | LM Studio | Q4_K_M GGUF | 7.1 GB | fits in fast memory | ~47 tok/s | lms get unsloth/Qwen3.5-9B-GGUF@Q4_K_M |
| RTX 3070 8GB | LM Studio | Q4_K_M GGUF | 7.1 GB | fits in fast memory | ~47 tok/s | lms get unsloth/Qwen3.5-9B-GGUF@Q4_K_M |
| RTX 4060 8GB | LM Studio | Q4_K_M GGUF | 7.1 GB | fits in fast memory | ~29 tok/s | lms get unsloth/Qwen3.5-9B-GGUF@Q4_K_M |
| RX 7600 8GB | LM Studio | Q4_K_M GGUF | 7.1 GB | fits in fast memory | ~30 tok/s | lms get unsloth/Qwen3.5-9B-GGUF@Q4_K_M |
| RTX 3080 10GB | Ollama | Q4_K_M | 8 GB | fits in fast memory | ~69 tok/s | ollama run qwen3.5:9b-q4_K_M |
| M1 16GB | Ollama | Q4_K_M | 8 GB | fits in fast memory | ~8 tok/s | ollama run qwen3.5:9b-q4_K_M |
| M2 16GB | Ollama | Q4_K_M | 8 GB | fits in fast memory | ~12 tok/s | ollama run qwen3.5:9b-q4_K_M |
| M3 16GB | Ollama | Q4_K_M | 8 GB | fits in fast memory | ~12 tok/s | ollama run qwen3.5:9b-q4_K_M |
| M4 16GB | Ollama | Q4_K_M | 8 GB | fits in fast memory | ~15 tok/s | ollama run qwen3.5:9b-q4_K_M |
| RTX 3060 12GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~22 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 4070 12GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~30 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 5070 12GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~40 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 4060 Ti 16GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~17 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 4070 Ti Super 16GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~40 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 4080 16GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~43 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 5080 16GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~58 tok/s | ollama run qwen3.5:9b-q8_0 |
| RX 7800 XT 16GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~37 tok/s | ollama run qwen3.5:9b-q8_0 |
| RX 9070 XT 16GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~38 tok/s | ollama run qwen3.5:9b-q8_0 |
| M2 24GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~8 tok/s | ollama run qwen3.5:9b-q8_0 |
| M3 24GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~8 tok/s | ollama run qwen3.5:9b-q8_0 |
| M4 24GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~10 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 3090 24GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~56 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 4090 24GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~60 tok/s | ollama run qwen3.5:9b-q8_0 |
| RX 7900 XTX 24GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~58 tok/s | ollama run qwen3.5:9b-q8_0 |
| M1 Pro 32GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~16 tok/s | ollama run qwen3.5:9b-q8_0 |
| M2 Pro 32GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~16 tok/s | ollama run qwen3.5:9b-q8_0 |
| M4 32GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~10 tok/s | ollama run qwen3.5:9b-q8_0 |
| M3 Pro 36GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~12 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M4 Max (32c GPU) 36GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~33 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M5 Max (32c GPU) 36GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~37 tok/s | ollama run qwen3.5:9b-q8_0 |
| RTX 5090 32GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~108 tok/s | ollama run qwen3.5:9b-q8_0 |
| M4 Pro 48GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~22 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M4 Max (40c GPU) 48GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~44 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M5 Max (40c GPU) 48GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~49 tok/s | ollama run qwen3.5:9b-q8_0 |
| M1 Max 64GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~32 tok/s | ollama run qwen3.5:9b-q8_0 |
| M2 Max 64GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~32 tok/s | ollama run qwen3.5:9b-q8_0 |
| M3 Max 64GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~32 tok/s | ollama run qwen3.5:9b-q8_0 |
| M4 Max 64GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~44 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M4 Max (40c GPU) 64GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~44 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M5 Max (40c GPU) 64GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~49 tok/s | ollama run qwen3.5:9b-q8_0 |
| DGX Spark 64GB (ASUS, Dell, HP, MSI, Acer, Gigabyte) | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~20 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M3 Ultra 96GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~66 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M5 Ultra 96GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~96 tok/s | ollama run qwen3.5:9b-q8_0 |
| M3 Max 128GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~32 tok/s | ollama run qwen3.5:9b-q8_0 |
| M4 Max 128GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~44 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M4 Max (40c GPU) 128GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~44 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M5 Max (40c GPU) 128GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~49 tok/s | ollama run qwen3.5:9b-q8_0 |
| DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte) | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~20 tok/s | ollama run qwen3.5:9b-q8_0 |
| M2 Ultra 192GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~64 tok/s | ollama run qwen3.5:9b-q8_0 |
| 2x Mac Studio M5 Max (40c GPU) 128GB | llama.cpp | Q8_0 GGUF | 11 GB | fits in fast memory | ~44 tok/s | llama-server -hf unsloth/Qwen3.5-9B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node |
| Mac Studio M3 Ultra 256GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~66 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M5 Ultra (64c GPU) 256GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~96 tok/s | ollama run qwen3.5:9b-q8_0 |
| Mac Studio M5 Ultra (80c GPU) 256GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~96 tok/s | ollama run qwen3.5:9b-q8_0 |
| 2x DGX Spark 128GB | llama.cpp | Q8_0 GGUF | 11 GB | fits in fast memory | ~18 tok/s | llama-server -hf unsloth/Qwen3.5-9B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node |
| 4x Mac Studio M5 Max (40c GPU) 128GB | llama.cpp | Q8_0 GGUF | 11 GB | fits in fast memory | ~39 tok/s | llama-server -hf unsloth/Qwen3.5-9B-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) 256GB | llama.cpp | Q8_0 GGUF | 11 GB | fits in fast memory | ~86 tok/s | llama-server -hf unsloth/Qwen3.5-9B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node |
| Mac Studio M3 Ultra (80c GPU) 512GB | Ollama | Q8_0 | 11.4 GB | fits in fast memory | ~66 tok/s | ollama run qwen3.5:9b-q8_0 |
| 4x DGX Spark 128GB | llama.cpp | Q8_0 GGUF | 11 GB | fits in fast memory | ~16 tok/s | llama-server -hf unsloth/Qwen3.5-9B-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) 256GB | llama.cpp | Q8_0 GGUF | 11 GB | fits in fast memory | ~76 tok/s | llama-server -hf unsloth/Qwen3.5-9B-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 2026-10-18.
A project by OnDevice Labs · llms.txt