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No. Mixtral 8x7B (MoE) does not fit on RTX 3060 Ti 8GB: Q4_K_M needs 29.8 GB and 7.5 GB is usable.
Hardware: RTX 3060 Ti 8GB · Model: Mixtral 8x7B (MoE)
RTX 3060 Ti 8GB has 8 GB of VRAM and 448 GB/s of memory bandwidth. RTX 3070 8GB has the same memory and bandwidth, so the fit numbers match; this page is RTX 3060 Ti 8GB. The checker holds back 0.5 GB of VRAM for the driver and assumes 16 GB of system RAM, so fast memory usable by Ollama is 7.5 GB. Partial offload may also use 60% of that system RAM. Mixtral 8x7B (MoE) (46.7B, 12.9B active, Mistral) at Q4_K_M is a 28 GB download and needs 29.8 GB with headroom for about 8k tokens.
| Best quant | Q4_K_M |
|---|---|
| Download size | 28 GB |
| Memory needed | 29.8 GB |
| Usable fast memory | 7.5 GB |
| Fit | does not fit |
| Estimated speed | no estimate, it does not fit |
| Command | ollama run mixtral:8x7b-instruct-v0.1-q4_K_M |
Smallest hardware that runs it: RTX 3090 24GB at Q4_K_M (partial offload (slower), 29.8 GB needed, 23.5 GB usable, about 10 tok/s (estimate)). Can a RTX 3090 24GB run Mixtral 8x7B (MoE)?
A smaller Mistral model that fits this hardware: Devstral Small 2 24B at Q4_K_M (partial offload (slower), 16.8 GB). Can a RTX 3060 Ti 8GB run Devstral Small 2 24B?
Open RTX 3060 Ti 8GB in the checkerEstimates, 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 2026-10-23.
A project by OnDevice Labs · llms.txt