text model · Yi · Windows
Can I run Yi 1.5 34B on 16GB RAM Laptop (CPU/iGPU only)?
No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but 16GB RAM Laptop (CPU/iGPU only) only has ~12 GB usable.
Needs ~21.4 GB even at Q4_K_M, but only ~12 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 9.4 GB: Yi 1.5 34B needs roughly 21.4 GB at Q4_K_M and 16GB RAM Laptop (CPU/iGPU only) leaves only about 12 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).
- Q4_K_M needed
- ~21.4 GB
- Usable on device
- ~12 GB
- Device memory
- 16 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
- 16 GB ram
- Usable for weights
- ~12 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
Run Yi 1.5 34B on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run Yi 1.5 34B?
No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but 16GB RAM Laptop (CPU/iGPU only) only has ~12 GB usable.
How much memory does Yi 1.5 34B need?
16GB RAM Laptop (CPU/iGPU only) 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/laptop-16gb) 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.