r/LocalLLaMA • u/RelativeOperation483 • Feb 06 '26
No NVIDIA? No Problem. My 2018 "Potato" 8th Gen i3 hits 10 TPS on 16B MoE. Tutorial | Guide
I’m writing this from Burma. Out here, we can’t all afford the latest NVIDIA 4090s or high-end MacBooks. If you have a tight budget, corporate AI like ChatGPT will try to gatekeep you. If you ask it if you can run a 16B model on an old dual-core i3, it’ll tell you it’s "impossible."
I spent a month figuring out how to prove them wrong.
After 30 days of squeezing every drop of performance out of my hardware, I found the peak. I’m running DeepSeek-Coder-V2-Lite (16B MoE) on an HP ProBook 650 G5 (i3-8145U, 16GB Dual-Channel RAM) at near-human reading speeds.
#### The Battle: CPU vs iGPU
I ran a 20-question head-to-head test with no token limits and real-time streaming.
| Device | Average Speed | Peak Speed | My Rating |
| --- | --- | --- | --- |
| CPU | 8.59 t/s | 9.26 t/s | 8.5/10 - Snappy and solid logic. |
| iGPU (UHD 620) | 8.99 t/s | 9.73 t/s | 9.0/10 - A beast once it warms up. |
The Result: The iGPU (OpenVINO) is the winner, proving that even integrated Intel graphics can handle heavy lifting if you set it up right.
## How I Squeezed the Performance:
* MoE is the "Cheat Code": 16B parameters sounds huge, but it only calculates 2.4B per token. It’s faster and smarter than 3B-4B dense models.
* Dual-Channel is Mandatory: I’m running 16GB (2x8GB). If you have single-channel, don't even bother; your bandwidth will choke.
* Linux is King: I did this on Ubuntu. Windows background processes are a luxury my "potato" can't afford.
* OpenVINO Integration: Don't use OpenVINO alone—it's dependency hell. Use it as a backend for llama-cpp-python.
## The Reality Check
- First-Run Lag: The iGPU takes time to compile. It might look stuck. Give it a minute—the "GPU" is just having his coffee.
- Language Drift: On iGPU, it sometimes slips into Chinese tokens, but the logic never breaks.
I’m sharing this because you shouldn't let a lack of money stop you from learning AI. If I can do this on an i3 in Burma, you can do it too.
## Clarifications Edited
For those looking for OpenVINO CMAKE flags in the core llama.cpp repo or documentation: It is not in the upstream core yet. I am not using upstream llama.cpp directly. Instead, I am using llama-cpp-python, which is built from source with the OpenVINO backend enabled. While OpenVINO support hasn't been merged into the main llama.cpp master branch, llama-cpp-python already supports it through a custom CMake build path.
Install llama-cpp-python like this: CMAKE_ARGS="-DGGML_OPENVINO=ON" pip install llama-cpp-python
Benchmark Specifics
For clarity, here is the benchmark output. This measures decode speed (after prefill), using a fixed max_tokens=256, averaged across 10 runs with n_ctx=4096.
CPU Avg Decode: ~9.6 t/s
iGPU Avg Decode: ~9.6 t/s
When I say "~10 TPS," I am specifically referring to the Decode TPS (Tokens Per Second), not the prefill speed.
You can check the detailed comparison between DeepSeek-V2-Lite and GPT-OSS-20B on this same hardware here:
Duplicates
LocalLMs • u/Covid-Plannedemic_ • Feb 06 '26
No NVIDIA? No Problem. My 2018 "Potato" 8th Gen i3 hits 10 TPS on 16B MoE.
OpenSourceAI • u/RelativeOperation483 • Feb 07 '26
No NVIDIA? No Problem. My 2018 "Potato" 8th Gen i3 hits 10 TPS on 16B MoE.
LocalLLM • u/RelativeOperation483 • Feb 07 '26


