text model · OpenELM · Windows
Can I run Apple OpenELM 1.1B on 16GB RAM Laptop (CPU/iGPU only)?
Yes. Apple OpenELM 1.1B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.7 GB of ~12 GB usable).
Runs at Q4_K_M using ~1.7 GB of ~12 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. 16GB RAM Laptop (CPU/iGPU only) leaves ~10.3 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.7 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~28 W
- Electricity / 1M tokens
- ~$0.02
- Pays for itself after
- ~1,563M tok
At ~$0.15/kWh and the estimated ~71 tok/s, a million generated tokens costs about $0.02 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$750 16GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 1,563 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf mradermacher/OpenELM-1_1B-GGUF:Q4_K_M lms get mradermacher/OpenELM-1_1B-GGUF How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 1.1B
- Q4_K_M size
- 0.63 GB
- Q8_0 size
- 1.07 GB
- Context
- 2k
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run Apple OpenELM 1.1B on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run Apple OpenELM 1.1B?
Yes. Apple OpenELM 1.1B runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.7 GB of ~12 GB usable).
How much memory does Apple OpenELM 1.1B need?
16GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~0.63 GB; with KV cache and runtime overhead, budget ~1.7 GB at a 4k context.
What is the best tool to run Apple OpenELM 1.1B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/openelm-1.1b/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.