From the time of release, r/LocalLLaMA focused on two issues: weights and quantization (3.55 TB for the full BF16 model, 2.05 TB for FP8, with official INT8/FP8 quantized versions) and whose chips power the "AI ASIC superpods" (the community inferred Huawei Ascend 910C from the Huawei HCCL acknowledgment and the term "superpod"). These are important community signals for judging whether LongCat-2.0 can be deployed in practice.
The weight-release post provided links to the official quantized versions:
Comment (bonobomaster): "Damn, that's a really long Cat! 3.55 TB in all its BF16 glory. 2.05 TB in FP8." — 3.55 TB for the full BF16 model and 2.05 TB for FP8.
The 1.6T/48B open-source announcement post (1unyvnz) provided links to the official three-part set: the HF model card, release posts on X from @eliebakouch and @ModelScope2022, and the official technical blog https://longcat.chat/blog/longcat-2.0/.
The post observed: "The model is 3.55 TB in BF16, an absolute behemoth… My conclusion is that 'a credible non-Nvidia supply chain exists at frontier scale already.' But the post never names a chip maker or model, consistently using the phrase 'domestic AI compute chips.'"
Community consensus: Most likely Huawei Ascend—"Almost certainly Huawei ascend" (RuthlessCriticismAll); "Quite possibly Ascends, unless another Chinese startup has entered the picture" (Admirable_Market2759); Huawei promoted its clusters using the term "superpod" in March 2026 (RhubarbSimilar1683 attached a Huawei news link).
Supporting evidence (from AlphaSignal's independent analysis, Evaluation 04): The official acknowledgments mention Huawei's HCCL communications library, and an independent estimate points to Ascend 910C.
The chip attribution is a community inference (Huawei 910C), not confirmed by the official source; cite it as "speculation."
The 3.55 TB (BF16) / 2.05 TB (FP8) sizes mean that the full model cannot be loaded on a single personal machine. The official documentation's deployment path uses SGLang across multiple nodes (Prompts directory 02), and even the quantized versions are realistic only for multi-GPU clusters.
Applicability by task: Anyone interested in running LongCat-2.0 locally should first check the FP8/INT8 quantized cards and VRAM requirements before deciding whether it is worthwhile. For most users, using an API/OpenRouter free endpoint is more practical.
LongCat 2.0
