I've been wanting to run some of the newer MoE models locally, particularly DeepSeek V4.1 Flash and GLM 5.3 Flash. The problem is that Intel GPU support for these models is still pretty far behind NVIDIA. I was already running some models through patched llama.cpp SYCL builds, but wanted something that could take better advantage of Intel's hardware. So I started working on XeMaya, an open-source inference engine built on XeStrata, using native SYCL, Level Zero, and Intel XMX acceleration. The goal is essentially to bring Project Maya's model support and inference approach to Intel Arc. I just released the first public alpha. GitHub: https://github.com/Jumbomuffin777/XeMaya My hardware: Ryzen 9 9950X Intel Arc Pro B70 32GB Intel Arc Pro B65 32GB Intel Arc Pro B60 24GB 128GB DDR5-4800 Ubuntu Linux 88GB total VRAM Benchmarks (prefill / generation, tok/s): DeepSeek V4.1 Flash (regular, 384 experts) Before (patched llama.cpp SYCL + Maya-XS): 45.6 / 10.9 XeMaya (B70 + B65): 11.4 / 14.56 XeMaya (all three GPUs): 11.2 / 11.58 This is the larger 129GB model, running from a PCIe Gen 3 NVMe. The previous configuration isn't directly comparable, but generation has improved on the two-GPU setup. Prefill is still a major bottleneck. Interestingly, two GPUs are actually faster than three here because the B60 is connected through a slower chipset link. DeepSeek V4.1 Flash Coder Initial XeMaya implementation: 48.5–48.9 / 16.3 Optimized XeMaya: 224.8 / 21.7 This was probably one of the more satisfying improvements. The newer implementation uses chunked prefill, direct GPU expert execution, and XMX attention. The model also matched the llama.cpp reference on all 16 tokens across both saved test prompts. GLM 5.3 Flash (Maya-S v2) Stock llama.cpp SYCL: 68.3 / 13.6 Experimental grouped-matmul SYCL: 108.9 / 14.0 XeMaya: 152–161 / 17.4 XeMaya with MTP: ~20 tok/s generation Qwen3.8 Flash-Next Upstream Strata: 2,211 prefill / 78.3 code generation / 59.2 prose generation XeMaya: 1,842–1,884 prefill / 76.6 code generation / 57.1 prose generation Strata is still ahead on Qwen, so there's definitely more optimization to do. These are recorded development benchmarks, not a fully controlled comparison. Some runs use different prompts, quantizations, GPU configurations, and context lengths. The optimized Coder numbers are also from development builds, while the published alpha has its own validation results. Current state: XeMaya currently has native inference paths for DeepSeek V4.1 Flash, DeepSeek Coder, GLM 5.3 Flash, and Qwen3.8 Flash-Next. It's still an early alpha, and there's plenty left to optimize, particularly expert caching, prefill performance, and multi-GPU scheduling. Right now the release is focused on Linux and Intel Arc. I also want to give proper credit to the developers whose work made this possible: MistVVK, creator of XeStrata, which provides the Intel-native foundation XeMaya is built on. Niko1221, creator of Strata, the original inference engine behind XeStrata. mw00, creator of Project Maya, whose work on DeepSeek, GLM, and Maya quantizations helped inspire this project. Georgi Gerganov and the ggml/llama.cpp contributors, whose implementations have been invaluable for testing correctness and comparing model outputs. A lot of the underlying work comes from these projects and their contributors, and Fable 5.2. I'm sharing this because I know there are other people trying to run newer models on Intel hardware. If anyone has an Arc GPU and wants to test XeMaya, I'd be interested in hearing how it performs, especially on different configurations. Repository and benchmarks: https://github.com/Jumbomuffin777/XeMaya   submitted by   /u/Jumbomuffin1312 [link]   [comments]