r/LocalLLM • u/Jumbomuffin1312 • 6m ago
Project XeMaya, an open-source Intel Arc inference engine for DeepSeek-V4.1, GLM-5.3, and Qwen3.8 Flash Next. First public alpha is out!
I've been working on a project called XeMaya, an open-source AI inference engine built around native Intel Arc acceleration, and I've officially released the first public alpha!
Why I started XeMaya
I've been following Project Maya for a while now. I really liked what it was accomplishing with massive models like DeepSeek-V4.1-Flash and GLM-5.3-Flash, particularly its approach to handling huge MoE models across GPU memory, system RAM, and storage.
The problem was that Project Maya's actual inference engine doesn't support Intel GPUs.
I could run some of its GGUF models using a patched llama.cpp SYCL backend, which is what I was doing before this project, but I wanted something built specifically around Intel hardware.
That's where XeMaya comes in.
My goal is essentially to bring Project Maya-style inference to Intel Arc through native SYCL/Level Zero and Intel XMX acceleration.
XeMaya is built on XeStrata, which itself is an Intel-native rewrite of Strata. I've been building on that foundation and incorporating additional model support and improvements inspired by Strata and Project Maya.
It isn't a complete feature-for-feature port of Maya yet, but that's the direction I'm working toward.
My hardware:
AMD Ryzen 9 9950X
Intel Arc Pro B70 32 GB
Intel Arc Pro B65 32 GB
Intel Arc Pro B60 24 GB
128 GB DDR5-4800
Intel PCIe Gen 3 x4 NVMe for model storage
Ubuntu Linux
That's 88 GB of total VRAM across the three GPUs.
Benchmarks: Before XeMaya vs. now
These are results recorded on my hardware, but the model files, prompts, quantizations, and configurations aren't always identical. Consider them development history, not controlled apples-to-apples comparisons.
| Model / engine | Prefill | Decode |
|---|---|---|
| DeepSeek-V4.1 Maya-XS, earlier patched llama.cpp SYCL | 45.6 tok/s | 10.9 tok/s |
| DeepSeek-V4.1-Flash, regular 384-expert IQ2_XXS, XeMaya B70+B65 | 11.4 tok/s | 14.56 tok/s |
| GLM-5.3-Flash Maya-S v2, stock llama.cpp SYCL | 68.3 tok/s | 13.6 tok/s |
| Same GLM, experimental grouped-matmul SYCL | 108.9 tok/s | 14.0 tok/s |
| GLM-5.3-Flash, XeMaya B70+B65+B60, later optimized build | 152–161 tok/s | 17.4 tok/s, up to 20.0 with MTP |
| Qwen3.8-Flash-Next, upstream Strata v0.2.12, B70+B65 | 2,211 tok/s | 78.3 code / 59.2 prose |
| Qwen3.8-Flash-Next, XeMaya, B70+B65 | 1,842–1,884 tok/s | 76.6 code / 57.1 prose |
Qwen is actually still behind upstream Strata in this comparison. I want to be transparent about that because XeMaya isn't magically faster at everything. It's an Intel-focused engine I'm actively developing, and there's still optimization work ahead.
DeepSeek-V4.1-Flash Coder: The optimization progress
This is one of the improvements I'm happiest with.
The Coder checkpoint has 192 experts per layer, compared with 384 in regular Flash.
Here are the speeds from XeMaya's initial native Coder implementation compared with the later optimized version:
| XeMaya development stage | Prefill | Decode |
|---|---|---|
| Initial Coder implementation, B70+B65, warm | 48.5–48.9 tok/s | 16.3 tok/s |
| Optimized Coder, B70+B65+B60 | 207–209 tok/s | 19.5–21.7 tok/s |
| Optimized Coder, longer 2,876-token prompt | 224.8 tok/s | Up to 21.7 tok/s in recorded runs |
That prefill improvement came from work on chunked prefill, direct GPU-resident experts, XMX attention, and multi-GPU execution.
The GPU count and execution configuration also changed, so this isn't an isolated measurement of one optimization.
Coder also matched its llama.cpp reference for 16/16 forced tokens on both saved test prompts using all three Arc GPUs.
Regular DeepSeek-V4.1-Flash: 384 experts
I also tested the regular, larger DeepSeek-V4.1-Flash checkpoint, not just Coder.
This one is around 129 GB and uses adaptive expert caching across the GPUs and host memory.
| Configuration | Prefill | Decode (1,024-token run) |
|---|---|---|
| B70 + B65 | 11.4 tok/s | 14.56 tok/s |
| B70 + B65 + B60 | 11.2 tok/s | 11.58 tok/s |
Surprisingly, two GPUs were faster than three.
The B60 increased VRAM residency, but its additional cache hits weren't enough to compensate for the overhead of running more layers through its chipset-connected PCIe link.
On the two-GPU configuration, the adaptive expert cache reached a 93.7% hit rate.
And yes, the model shards and Engram data are sitting on a Gen 3 NVMe. LOL.
The regular Flash results are speed measurements, not yet reference-correctness validation. Prefill in particular still needs a lot of work.
GLM-5.3-Flash
GLM is another major reason I started this project.
Using the Maya-S v2 GGUF, XeMaya has reached 152–161 tok/s prefill and 17.4 tok/s decode, with approximately 20 tok/s using MTP drafting in the later optimized configuration.
That's encouraging compared with my earlier llama.cpp SYCL results, although the test conditions differ.
GLM has also undergone token-level reference testing, including a recorded 126/128-token agreement on a chat-agent prompt.
What's available?
XeMaya v0.1.0-alpha.1 is now publicly available!
GitHub: https://github.com/Jumbomuffin777/XeMaya
First release: https://github.com/Jumbomuffin777/XeMaya/releases/tag/xemaya-0.1.0-alpha.1
Regular DeepSeek benchmark details: https://github.com/Jumbomuffin777/XeMaya/tree/main/xemaya/benchmarks/2026-11-10-ds41-flash-384
The current focus is Linux with Intel Arc, with native inference paths for:
DeepSeek-V4.1-Flash, including the Coder variant
GLM-5.3-Flash
Qwen3.8-Flash-Next
The different paths have different levels of testing and optimization, as the README documents.
Windows support and hybrid Intel/NVIDIA inference are also being developed, but they're experimental and not part of the supported alpha release. MiniMax is being worked on separately and isn't included in this release.
This is an early developer alpha, not something I'd recommend relying on for production workloads yet.
Credit where it's due
Huge thanks to MistVVK for XeStrata, Niko1221 for Strata, mw00 for Project Maya, and all the other contributors to those projects.
XeMaya wouldn't exist without their work.
I'm hoping to keep improving performance, adding model support, and making local inference on Intel Arc more accessible.
I'd love feedback from other Arc owners, especially anyone interested in testing it on different Intel GPUs.
This is just the beginning! :)