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Can I run Llama 3.1 8B locally?

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Ollama weight files: Q4_K_M 4.9 GB (llama3.1:8b); Q5_K_M 5.7 GB (llama3.1:8b-instruct-q5_K_M); Q8_0 8.5 GB (llama3.1:8b-instruct-q8_0).

Yes on 59 machines in this list. The smallest is RTX 3060 Ti 8GB: Q5_K_M, about 6.7 GB, roughly 47 tokens per second, via Ollama. ollama run llama3.1:8b-instruct-q5_K_M

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

Open Llama 3.1 8B in the checker

Hardware that fits

HardwareRuntimeQuantMemoryFitSpeedCommand
RTX 3060 Ti 8GBOllamaQ5_K_M6.7 GBfits in fast memory~47 tok/sollama run llama3.1:8b-instruct-q5_K_M
RTX 3070 8GBOllamaQ5_K_M6.7 GBfits in fast memory~47 tok/sollama run llama3.1:8b-instruct-q5_K_M
RTX 4060 8GBOllamaQ5_K_M6.7 GBfits in fast memory~29 tok/sollama run llama3.1:8b-instruct-q5_K_M
RX 7600 8GBOllamaQ5_K_M6.7 GBfits in fast memory~30 tok/sollama run llama3.1:8b-instruct-q5_K_M
RTX 3080 10GBOllamaQ8_09.5 GBfits in fast memory~54 tok/sollama run llama3.1:8b-instruct-q8_0
M1 16GBOllamaQ8_09.5 GBfits in fast memory~6 tok/sollama run llama3.1:8b-instruct-q8_0
M2 16GBOllamaQ8_09.5 GBfits in fast memory~9 tok/sollama run llama3.1:8b-instruct-q8_0
M3 16GBOllamaQ8_09.5 GBfits in fast memory~9 tok/sollama run llama3.1:8b-instruct-q8_0
M4 16GBOllamaQ8_09.5 GBfits in fast memory~11 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 3060 12GBOllamaQ8_09.5 GBfits in fast memory~25 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 4070 12GBOllamaQ8_09.5 GBfits in fast memory~36 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 5070 12GBOllamaQ8_09.5 GBfits in fast memory~47 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 4060 Ti 16GBOllamaQ8_09.5 GBfits in fast memory~20 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 4070 Ti Super 16GBOllamaQ8_09.5 GBfits in fast memory~47 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 4080 16GBOllamaQ8_09.5 GBfits in fast memory~51 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 5080 16GBOllamaQ8_09.5 GBfits in fast memory~68 tok/sollama run llama3.1:8b-instruct-q8_0
RX 7800 XT 16GBOllamaQ8_09.5 GBfits in fast memory~44 tok/sollama run llama3.1:8b-instruct-q8_0
RX 9070 XT 16GBOllamaQ8_09.5 GBfits in fast memory~45 tok/sollama run llama3.1:8b-instruct-q8_0
M2 24GBOllamaQ8_09.5 GBfits in fast memory~9 tok/sollama run llama3.1:8b-instruct-q8_0
M3 24GBOllamaQ8_09.5 GBfits in fast memory~9 tok/sollama run llama3.1:8b-instruct-q8_0
M4 24GBOllamaQ8_09.5 GBfits in fast memory~11 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 3090 24GBOllamaQ8_09.5 GBfits in fast memory~66 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 4090 24GBOllamaQ8_09.5 GBfits in fast memory~71 tok/sollama run llama3.1:8b-instruct-q8_0
RX 7900 XTX 24GBOllamaQ8_09.5 GBfits in fast memory~68 tok/sollama run llama3.1:8b-instruct-q8_0
M1 Pro 32GBOllamaQ8_09.5 GBfits in fast memory~19 tok/sollama run llama3.1:8b-instruct-q8_0
M2 Pro 32GBOllamaQ8_09.5 GBfits in fast memory~19 tok/sollama run llama3.1:8b-instruct-q8_0
M4 32GBOllamaQ8_09.5 GBfits in fast memory~11 tok/sollama run llama3.1:8b-instruct-q8_0
M3 Pro 36GBOllamaQ8_09.5 GBfits in fast memory~14 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M4 Max (32c GPU) 36GBOllamaQ8_09.5 GBfits in fast memory~39 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M5 Max (32c GPU) 36GBOllamaQ8_09.5 GBfits in fast memory~43 tok/sollama run llama3.1:8b-instruct-q8_0
RTX 5090 32GBOllamaQ8_09.5 GBfits in fast memory~126 tok/sollama run llama3.1:8b-instruct-q8_0
M4 Pro 48GBOllamaQ8_09.5 GBfits in fast memory~26 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M4 Max (40c GPU) 48GBOllamaQ8_09.5 GBfits in fast memory~51 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M5 Max (40c GPU) 48GBOllamaQ8_09.5 GBfits in fast memory~58 tok/sollama run llama3.1:8b-instruct-q8_0
M1 Max 64GBOllamaQ8_09.5 GBfits in fast memory~38 tok/sollama run llama3.1:8b-instruct-q8_0
M2 Max 64GBOllamaQ8_09.5 GBfits in fast memory~38 tok/sollama run llama3.1:8b-instruct-q8_0
M3 Max 64GBOllamaQ8_09.5 GBfits in fast memory~38 tok/sollama run llama3.1:8b-instruct-q8_0
M4 Max 64GBOllamaQ8_09.5 GBfits in fast memory~51 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M4 Max (40c GPU) 64GBOllamaQ8_09.5 GBfits in fast memory~51 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M5 Max (40c GPU) 64GBOllamaQ8_09.5 GBfits in fast memory~58 tok/sollama run llama3.1:8b-instruct-q8_0
DGX Spark 64GB (ASUS, Dell, HP, MSI, Acer, Gigabyte)OllamaQ8_09.5 GBfits in fast memory~24 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M3 Ultra 96GBOllamaQ8_09.5 GBfits in fast memory~77 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M5 Ultra 96GBOllamaQ8_09.5 GBfits in fast memory~113 tok/sollama run llama3.1:8b-instruct-q8_0
M3 Max 128GBOllamaQ8_09.5 GBfits in fast memory~38 tok/sollama run llama3.1:8b-instruct-q8_0
M4 Max 128GBOllamaQ8_09.5 GBfits in fast memory~51 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M4 Max (40c GPU) 128GBOllamaQ8_09.5 GBfits in fast memory~51 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M5 Max (40c GPU) 128GBOllamaQ8_09.5 GBfits in fast memory~58 tok/sollama run llama3.1:8b-instruct-q8_0
DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)OllamaQ8_09.5 GBfits in fast memory~24 tok/sollama run llama3.1:8b-instruct-q8_0
M2 Ultra 192GBOllamaQ8_09.5 GBfits in fast memory~75 tok/sollama run llama3.1:8b-instruct-q8_0
2x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ5_K_M GGUF6.7 GBfits in fast memory~73 tok/sllama-server -hf unsloth/Llama-3.1-8B-Instruct-GGUF:Q5_K_M --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra 256GBOllamaQ8_09.5 GBfits in fast memory~77 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M5 Ultra (64c GPU) 256GBOllamaQ8_09.5 GBfits in fast memory~113 tok/sollama run llama3.1:8b-instruct-q8_0
Mac Studio M5 Ultra (80c GPU) 256GBOllamaQ8_09.5 GBfits in fast memory~113 tok/sollama run llama3.1:8b-instruct-q8_0
2x DGX Spark 128GBllama.cppQ5_K_M GGUF6.7 GBfits in fast memory~30 tok/sllama-server -hf unsloth/Llama-3.1-8B-Instruct-GGUF:Q5_K_M --rpc node2:50052 -c 8192 # run rpc-server on each other node
4x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ5_K_M GGUF6.7 GBfits in fast memory~64 tok/sllama-server -hf unsloth/Llama-3.1-8B-Instruct-GGUF:Q5_K_M --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node
2x Mac Studio M5 Ultra (80c GPU) 256GBllama.cppQ5_K_M GGUF6.7 GBfits in fast memory~142 tok/sllama-server -hf unsloth/Llama-3.1-8B-Instruct-GGUF:Q5_K_M --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (80c GPU) 512GBOllamaQ8_09.5 GBfits in fast memory~77 tok/sollama run llama3.1:8b-instruct-q8_0
4x DGX Spark 128GBllama.cppQ5_K_M GGUF6.7 GBfits in fast memory~27 tok/sllama-server -hf unsloth/Llama-3.1-8B-Instruct-GGUF:Q5_K_M --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node
4x Mac Studio M5 Ultra (80c GPU) 256GBllama.cppQ5_K_M GGUF6.7 GBfits in fast memory~126 tok/sllama-server -hf unsloth/Llama-3.1-8B-Instruct-GGUF:Q5_K_M --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 2026-10-16.

A project by OnDevice Labs · llms.txt