[perf]: Load H3 DiT weights onto CUDA, not CPU

GB10 unified memory made the CPU-then-copy path take minutes for
the 35B FastH3 transformer. When host offload is off, read
safetensors onto the accelerator.

docs/getting_started/installation/spark_performance.md

@@ -161,6 +161,12 @@ is power-cycled. To avoid it:
   on: "CPU" offload uses the same unified RAM. Multi-GPU FSDP sharding remains
   available because it partitions weights without parking them in a separate
   host pool.
+- **MiniMax H3 / FastH3** still needs sequential loading on one GB10. The Qwen3-VL
+  conditioner is tens of gigabytes of BF16. If the DiT and VAEs load while that
+  encoder is still resident, the process is a typical `earlyoom` kill (Python is
+  preferred). The CUDA pipeline now encodes first, releases the encoder, then
+  loads DiT and VAEs onto the accelerator (`to_cpu` follows `cpu_offload`, which
+  is off here). See [Offloading](../../inference/offloading.md).
 
 ## Gotchas specific to the GB10