text model · Yi · Windows
Can I run Yi 1.5 34B on Nvidia GeForce RTX 3060 (12GB)?
No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but Nvidia GeForce RTX 3060 (12GB) only has ~11 GB usable.
Needs ~21.4 GB even at Q4_K_M, but only ~11 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 10.4 GB: Yi 1.5 34B needs roughly 21.4 GB at Q4_K_M and Nvidia GeForce RTX 3060 (12GB) leaves only about 11 GB usable for a model. The lightest tracked hardware that runs Yi 1.5 34B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Yi 1.5 34B on Nvidia GeForce RTX 4090 (24GB).
Too big for Nvidia GeForce RTX 3060 (12GB)'s VRAM, but you could offload some layers to system RAM and still run it at roughly ~3 tok/s, far slower than a model that fits. How this is estimated.
- Q4_K_M needed
- ~21.4 GB
- Usable on device
- ~11 GB
- Device memory
- 12 GB
Which quant fits
How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 34B
- Q4_K_M size
- 19.24 GB
- Q8_0 size
- 34.03 GB
- Context
- 32k
- Memory
- 12 GB vram
- Usable for weights
- ~11 GB
- Power draw
- ~170 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run Yi 1.5 34B on other hardware
FAQ
Can Nvidia GeForce RTX 3060 (12GB) run Yi 1.5 34B?
No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but Nvidia GeForce RTX 3060 (12GB) only has ~11 GB usable.
How much memory does Yi 1.5 34B need?
Nvidia GeForce RTX 3060 (12GB) does not have enough memory. At Q4_K_M the weights are ~19.24 GB; with KV cache and runtime overhead, budget ~21.4 GB at a 4k context.
What is the best tool to run Yi 1.5 34B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/yi-1.5-34b/nvidia-rtx-3060-12gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.