Has anyone gotten Llama.cpp (or other) working using Intel iGPU (arrowlake) where it actually improves anything?

I Recently did a bunch of tests and wrote them all up on here, but the short version is that Vulkan basically doesn't work (or when it does, it's at 1tok/s at best).

SYCL works pretty well, seems to run the Qwen3.6 35b models at around 12tok/s. The prefill part is around 20tok/s when it works but can be hit and miss and sometimes when you say "do that again" the llm just freezes up for ages.

CPU by itself is the most consistent at actually working, and runs at more like 14tok/s on those models, and 30-40tok/s on the prefill part.

So either the iGPU is totally useless (which I would believe), but there does seem to be reports of even older igpus pulling their weight at least.

Or I have something set up wrong.

This is running on an Unraid box, so I've tried the following setups -

Llama.cpp docker containers (I have 3 of them, CPU-only, Vulkan, SYCL)

LM-Studio running in Webtop (a linux container). I have tested inside the container and the igpu is recognised by lm-studio and ram/vram is detected and vulkan sees everything. It's just not very good.

Llama-swap docker containers (2 of them, cpu and vulkan). There isn't a SYCL one unfortunately. Or at least the one I could find doesn't work at all.

I'd appreciate some guidance.

EDIT:

CPU is a 285hx. 64gb of ram.

Vulkan:
Qwen3.6-35B-A3B-Q4_K_S-4.22bpw.gguf = 0.5 tk/s
Qwen3.6-35B-A3B-IQ4_XS-3.93bpw.gguf = 0.5 tk/s
gemma-4-26B-A4B-it-MXFP4_MOE.gguf = works but quite slow, 4tk/s

Sycl:
Qwen3.6-35B-A3B-Q4_K_S-4.22bpw.gguf = 8 tk/s
Qwen3.6-35B-A3B-IQ4_XS-3.93bpw.gguf = 12 tk/s
gemma-4-26B-A4B-it-MXFP4_MOE.gguf = 8 tk/s
Qwen3.6-35B-A3B-UD-Q6_K-Unsloth.gguf = 4tk/s
Qwen3.6-35B-A3B-IQ4_XS-3.97bpw-BS-MTP.gguf = 12tk/s

Cpu-only:
Qwen3.6-35B-A3B-Q4_K_S-4.22bpw.gguf = 14 tk/s
Qwen3.6-35B-A3B-IQ4_XS-3.93bpw.gguf = 14 tk/s
gemma-4-26B-A4B-it-MXFP4_MOE.gguf = 8 tk/s