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Can I run Nimble v3 9B (Bespoke Labs, decision) locally?

Bespoke Labs · 9B parameters · chat.

Ollama weight files: Q4_K_M 5.6 GB (nimble-v3:9b-q4_K_M); Q8_0 9.5 GB (nimble-v3:9b-q8_0).

Yes on 59 machines in this list. The smallest is RTX 3060 Ti 8GB: Q4_K_M, about 6.6 GB, roughly 48 tokens per second, via Ollama. ollama run nimble-v3:9b-q4_K_M

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

Open Nimble v3 9B (Bespoke Labs, decision) in the checker

Hardware that fits

HardwareRuntimeQuantMemoryFitSpeedCommand
RTX 3060 Ti 8GBOllamaQ4_K_M6.6 GBfits in fast memory~48 tok/sollama run nimble-v3:9b-q4_K_M
RTX 3070 8GBOllamaQ4_K_M6.6 GBfits in fast memory~48 tok/sollama run nimble-v3:9b-q4_K_M
RTX 4060 8GBOllamaQ4_K_M6.6 GBfits in fast memory~29 tok/sollama run nimble-v3:9b-q4_K_M
RX 7600 8GBOllamaQ4_K_M6.6 GBfits in fast memory~31 tok/sollama run nimble-v3:9b-q4_K_M
RTX 3080 10GBOllamaQ4_K_M6.6 GBfits in fast memory~81 tok/sollama run nimble-v3:9b-q4_K_M
M1 16GBOllamaQ8_010.6 GBfits in fast memory~6 tok/sollama run nimble-v3:9b-q8_0
M2 16GBOllamaQ8_010.6 GBfits in fast memory~8 tok/sollama run nimble-v3:9b-q8_0
M3 16GBOllamaQ8_010.6 GBfits in fast memory~8 tok/sollama run nimble-v3:9b-q8_0
M4 16GBOllamaQ8_010.6 GBfits in fast memory~10 tok/sollama run nimble-v3:9b-q8_0
RTX 3060 12GBOllamaQ8_010.6 GBfits in fast memory~23 tok/sollama run nimble-v3:9b-q8_0
RTX 4070 12GBOllamaQ8_010.6 GBfits in fast memory~32 tok/sollama run nimble-v3:9b-q8_0
RTX 5070 12GBOllamaQ8_010.6 GBfits in fast memory~42 tok/sollama run nimble-v3:9b-q8_0
RTX 4060 Ti 16GBOllamaQ8_010.6 GBfits in fast memory~18 tok/sollama run nimble-v3:9b-q8_0
RTX 4070 Ti Super 16GBOllamaQ8_010.6 GBfits in fast memory~42 tok/sollama run nimble-v3:9b-q8_0
RTX 4080 16GBOllamaQ8_010.6 GBfits in fast memory~45 tok/sollama run nimble-v3:9b-q8_0
RTX 5080 16GBOllamaQ8_010.6 GBfits in fast memory~61 tok/sollama run nimble-v3:9b-q8_0
RX 7800 XT 16GBOllamaQ8_010.6 GBfits in fast memory~39 tok/sollama run nimble-v3:9b-q8_0
RX 9070 XT 16GBOllamaQ8_010.6 GBfits in fast memory~40 tok/sollama run nimble-v3:9b-q8_0
M2 24GBOllamaQ8_010.6 GBfits in fast memory~8 tok/sollama run nimble-v3:9b-q8_0
M3 24GBOllamaQ8_010.6 GBfits in fast memory~8 tok/sollama run nimble-v3:9b-q8_0
M4 24GBOllamaQ8_010.6 GBfits in fast memory~10 tok/sollama run nimble-v3:9b-q8_0
RTX 3090 24GBOllamaQ8_010.6 GBfits in fast memory~59 tok/sollama run nimble-v3:9b-q8_0
RTX 4090 24GBOllamaQ8_010.6 GBfits in fast memory~64 tok/sollama run nimble-v3:9b-q8_0
RX 7900 XTX 24GBOllamaQ8_010.6 GBfits in fast memory~61 tok/sollama run nimble-v3:9b-q8_0
M1 Pro 32GBOllamaQ8_010.6 GBfits in fast memory~17 tok/sollama run nimble-v3:9b-q8_0
M2 Pro 32GBOllamaQ8_010.6 GBfits in fast memory~17 tok/sollama run nimble-v3:9b-q8_0
M4 32GBOllamaQ8_010.6 GBfits in fast memory~10 tok/sollama run nimble-v3:9b-q8_0
M3 Pro 36GBOllamaQ8_010.6 GBfits in fast memory~13 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M4 Max (32c GPU) 36GBOllamaQ8_010.6 GBfits in fast memory~35 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M5 Max (32c GPU) 36GBOllamaQ8_010.6 GBfits in fast memory~39 tok/sollama run nimble-v3:9b-q8_0
RTX 5090 32GBOllamaQ8_010.6 GBfits in fast memory~113 tok/sollama run nimble-v3:9b-q8_0
M4 Pro 48GBOllamaQ8_010.6 GBfits in fast memory~23 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M4 Max (40c GPU) 48GBOllamaQ8_010.6 GBfits in fast memory~46 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M5 Max (40c GPU) 48GBOllamaQ8_010.6 GBfits in fast memory~52 tok/sollama run nimble-v3:9b-q8_0
M1 Max 64GBOllamaQ8_010.6 GBfits in fast memory~34 tok/sollama run nimble-v3:9b-q8_0
M2 Max 64GBOllamaQ8_010.6 GBfits in fast memory~34 tok/sollama run nimble-v3:9b-q8_0
M3 Max 64GBOllamaQ8_010.6 GBfits in fast memory~34 tok/sollama run nimble-v3:9b-q8_0
M4 Max 64GBOllamaQ8_010.6 GBfits in fast memory~46 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M4 Max (40c GPU) 64GBOllamaQ8_010.6 GBfits in fast memory~46 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M5 Max (40c GPU) 64GBOllamaQ8_010.6 GBfits in fast memory~52 tok/sollama run nimble-v3:9b-q8_0
DGX Spark 64GB (ASUS, Dell, HP, MSI, Acer, Gigabyte)OllamaQ8_010.6 GBfits in fast memory~22 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M3 Ultra 96GBOllamaQ8_010.6 GBfits in fast memory~69 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M5 Ultra 96GBOllamaQ8_010.6 GBfits in fast memory~101 tok/sollama run nimble-v3:9b-q8_0
M3 Max 128GBOllamaQ8_010.6 GBfits in fast memory~34 tok/sollama run nimble-v3:9b-q8_0
M4 Max 128GBOllamaQ8_010.6 GBfits in fast memory~46 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M4 Max (40c GPU) 128GBOllamaQ8_010.6 GBfits in fast memory~46 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M5 Max (40c GPU) 128GBOllamaQ8_010.6 GBfits in fast memory~52 tok/sollama run nimble-v3:9b-q8_0
DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)OllamaQ8_010.6 GBfits in fast memory~22 tok/sollama run nimble-v3:9b-q8_0
M2 Ultra 192GBOllamaQ8_010.6 GBfits in fast memory~67 tok/sollama run nimble-v3:9b-q8_0
2x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF10.6 GBfits in fast memory~44 tok/sllama-server -hf ggml-org/Bespoke-Nimble-9B-v3-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra 256GBOllamaQ8_010.6 GBfits in fast memory~69 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M5 Ultra (64c GPU) 256GBOllamaQ8_010.6 GBfits in fast memory~101 tok/sollama run nimble-v3:9b-q8_0
Mac Studio M5 Ultra (80c GPU) 256GBOllamaQ8_010.6 GBfits in fast memory~101 tok/sollama run nimble-v3:9b-q8_0
2x DGX Spark 128GBllama.cppQ8_0 GGUF10.6 GBfits in fast memory~18 tok/sllama-server -hf ggml-org/Bespoke-Nimble-9B-v3-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 GGUF10.6 GBfits in fast memory~39 tok/sllama-server -hf ggml-org/Bespoke-Nimble-9B-v3-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 GGUF10.6 GBfits in fast memory~86 tok/sllama-server -hf ggml-org/Bespoke-Nimble-9B-v3-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (80c GPU) 512GBOllamaQ8_010.6 GBfits in fast memory~69 tok/sollama run nimble-v3:9b-q8_0
4x DGX Spark 128GBllama.cppQ8_0 GGUF10.6 GBfits in fast memory~16 tok/sllama-server -hf ggml-org/Bespoke-Nimble-9B-v3-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 GGUF10.6 GBfits in fast memory~76 tok/sllama-server -hf ggml-org/Bespoke-Nimble-9B-v3-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 2026-10-16.

A project by OnDevice Labs · llms.txt