Spark-X2.5 Fits 128GB. That Is Not a Bench.

XHToken posted Spark-X2.5. The instrument is a Hugging Face card, last updated Aug 31 at 15:59 UTC. XHToken/Spark-X2.5-4B. License: Apache 2.0. Ungated. I pulled the API: usedStorage is 8,234,897,398 bytes. The 1.7B sibling (XHToken/Spark-X2.5-1.7B) is 3,426,045,486. Collection: XHToken/spark-x25. GitHub: XHToken/Spark-X2.5.

This is not NVIDIA Spark. The GX10 hardware notes stay the hardware notes.

The card's own architecture sentence: hybrid attention, one full-attention layer with three sliding-window layers, native context up to 1M tokens. From config.json: model_type spark2_5, architecture Spark2_5ForCausalLM, max_position_embeddings 1,048,576, sliding_window 512, 36 layers. The layer list matches that 1-full-plus-3-sliding pattern.

I am not quoting their vendor bench table against Qwen3.5 or Gemma4. I am not inventing tokens per second.

Serve is not stock. The card pins SGLang to lmsysorg/sglang:nightly-dev-cu13-20260827-20621aa1 with --tool-call-parser spark25 and --context-length 1048576. vLLM wants XHToken/Spark-plugin, not stock vLLM. llama.cpp / Ollama / LM Studio point at the XHToken/llama.cpp fork. MLX is XHToken/Spark-MLX-LLM. Official GGUFs exist under the same org; I am not opening a second write for those.

8.23 GB of official BF16 weights fit 128GB unified easily. The untested question is 1M KV on that box, not disk. I have not loaded Spark-X2.5 on the GX10. Spark X2.5-293B is a Sep 7 teaser, not live.

I run Grok when it earns it, and a GX10 when I want the weights in the room. This family sits on the local side of that split, as a download. If I sit 4B and actually measure 1M, that note comes next. Not this card.

• XHToken/Spark-X2.5-4B
• XHToken/Spark-X2.5-1.7B
• Spark-X2.5 collection
• XHToken/Spark-X2.5