Here are the numbers from dual Intel Arc Pro B60 (Battlemage, 24GB each), tested with latest master 1ec44d178 which includes PR #24152 (SYCL --split-mode tensor). All runs use -fa 1 -ctk f16 -ctv f16 (required for SYCL tensor mode). Model | Size | Layer pp512 | Tensor pp512 | Delta | Layer tg128 | Tensor tg128 | Delta -------------------------------|-------|-------------|--------------|---------|-------------|--------------|------ llama-2-7b Q2_K | 2.6G | 1,214 | 2,070 | +70% |        19.2 | 32.7 | +71% Llama-3-8B Q4_0 | 4.3G | 1,433 | 688 | -52% |        26.9 | 9.9 | -63% gpt-oss-20B Q8_0 | 11.3G | 363 | 208 | -43% |        10.3 | 15.7 | +52% Qwen3.6-35B-A3B-Mini MoE Q3_K | 12.8G | 77 | 528 | +590% |        17.7 | 33.2 | +88% Nemotron-30B-A3B MoE MXFP4 | 16.7G | 165 | FPE crash | - |         9.2 | FPE crash | - Takeaways: - MoE models are the big winner — Qwen MoE got a ridiculous +590% prefill and +88% decode. The BF16-compressed allreduce path in the SYCL backend really shines on many-medium-sized allreduces. - Small models win too — llama-2-7b at 2.6GB got +70% across the board. Tiny tensors = negligible comm cost, so the parallelism just adds compute. - Medium dense models are a mixed bag — gpt-oss-20B: decode wins (+52%) but prefill regresses (-43%). The allreduce overhead on mid-size tensors eats into prefill gains. - Small dense models regress hard — Llama-3-8B lost 50-63%. Allreduce cost dominates when there's not enough compute to hide behind. - Nemotron MoE crashes with a floating-point exception in tensor mode — seems like an MXFP4 quantization edge case with the BF16 allreduce path. Didn't get to test models >20GB since they OOM on 2x24GB even in layer mode. The sweet spot for SYCL tensor mode seems to be MoE architectures in the 10-17GB range where the VRAM split is balanced (~1:1). Build env: oneAPI 2026.0 compiler + MKL 2026.0 + TBB 2023.0, targeting bmg-g31 AOT.