COcolorant·May 13, 2025Submission
FlashMoE: DeepSeek-R1 671B and Qwen3MoE 235B with 1~2 Intel B580 GPU in IPEX-LLM
2 points · Open discussionintel
ipex-llm
Python2016–2026
intel / ipex-llm · Archived
ipex-llm
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc.
12Distinct people
18Mentions
0Explicit recommenders
8.8KGitHub stars
What to know before exploring
What it is
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:04:58 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year18 dated mentions
Observed mentions: 2016-12-31 02:28:04 UTC to 2026-08-09 19:20:11 UTC.
* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 2 | 2 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 4 | 1 | 3 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 10 | 3 | 7 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
10 sourcesThat's because the OP is linking to the quickstart guide. There are benchmark numbers on the github's root page, but it does not appear to include the new deepseek yet:
https://github.com/intel/ipex-llm/tree/main?tab=readme-ov-fi...
In DeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on XeonSee this section https://github.com/intel/ipex-llm/blob/main/docs/mddocs/Quic...
In DeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on Xeonhttps://github.com/intel/ipex-llm/blob/main/docs/mddocs/Quic...
Requirements (>8 token/s):
380GB CPU Memory
1-8 ARC A770
500GB Disk
In DeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on XeonSince the Xeon alone could run the model in this set up it'd be more interesting if they compared the performance uplift with using 0/1/2..8 Arc A770 GPUs.
Also, it's probably better to link straight to the relevant section https://github.com/intel/ipex-llm/blob/main/docs/mddocs/Quic...
In DeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on XeonDeepSeek-R1-671B-Q4_K_M with 1 or 2 Arc A770 on Xeon
297 points · Open discussionyou have the choice to go with the AMD Ryzen AI Max processor which rivals the M4. And gives similar battery life and performance.
Or the Intel Lunar Lake processor.
Both have extremely good laptop options - the Lenovo Yoga Aura edition is pretty much macbook quality.
And runs LLMs (https://github.com/intel/ipex-llm/blob/main/docs/mddocs/Over...)
In MacBook Air M4IPEX-LLM Portable Zip for Ollama on Intel GPU
2 points · Open discussionThis article is coming out at an interesting time for me.
We probably have different definitions for "budget", but I just ordered a super janky eGPU setup for my very dated 8th gen Intel NUC, with a m2->pcie adapter, a PSU, and a refurb Intel A770 for about 350 all-in, not bad considering that's about the cost of a proper Thunderbolt eGPU enclosure alone.
The overall idea: A770 seems like a really good budget LLM GPU since it has more memory (16GB) and more memory bandwidth (512GB/s) than a 4070, but costs a tiny fraction. The m2-pcie adapter should give it a bit more bandwidth to the rest of the system than Thunderbolt as well, so hopefully it'll make for a decent gaming experience too.
If the eGPU part of the setup doesn't work out for some reason, I'll probably just bite the bullet and order the rest of the PC for a couple hundred more
Read the rest
, and return the m2-pcie adapter (I got it off of Amazon instead of Aliexpress specifically so I could do this), looking to end up somewhere around 600 bux total. I think that's probably a more reasonable price of entry for something like this for most people.
Curious if anyone else has experience with the A770 for LLM? Been looking at Intel's https://github.com/intel/ipex-llm project and it looked pretty promising, that's what made me pull the trigger in the end. Am I making a huge mistake?
There is ipex-llm support for Ollama on Intel GPU (https://github.com/intel/ipex-llm/blob/main/docs/mddocs/Quic...)
In Llama.cpp supports Vulkan. why doesn't Ollama?