LongCat-Next-w8a8-int8-GB10

LongCat-Next — w8a8_int8 for DGX Spark (GB10)

An 8-bit (w8a8_int8) quantization of Meituan LongCat-Next, packaged to serve every modality on a single NVIDIA GB10 system (sm_121) through one SGLang process: text generation, image understanding and generation, audio understanding and voice-clone generation, and video understanding. Developed and validated on a DGX Spark.

Serving code, Dockerfile, and an OpenAI-compatible gateway: https://github.com/InquiringMinds-AI/longcat-next-multimodal

What's in this repo

Self-contained (~90 GB) — nothing else to download:

• model-*-of-00015.safetensors (15 shards) — the backbone quantized to w8a8_int8: per-channel symmetric int8 weights with per-token dynamic int8 activations on the MoE experts and the n-gram over-embedding. Attention, router, generation heads, tokenizers, and decoders are kept at higher precision.
• model-smoothscale.safetensors — the per-channel SmoothQuant scales (α=0.5) for the MoE gate/up projections. These are load-bearing: the int8 expert gate/up weights were re-quantized with the scales absorbed (W·s) and the runtime divides the matching activations by smooth_scale, so (X/s)·(W·s) = X·W holds at int8. down_proj untouched.
• The image decoder (image_decoder/) and audio vocoder (cosy24k_vocoder/) required for generation, plus tokenizers, configs, and the trust-remote-code modeling files. Config weight-paths resolve in-directory.

Why 8-bit

Switching to 8-bit (w8a8_int8) is what made image and audio generation coherent: at 4-bit (NVFP4) both collapsed — images tiled into abstract texture, audio into a drone — and at 8-bit they came out faithful. That's why this checkpoint is 8-bit.

We did not re-test 4-bit after the later generation-path fixes and optimizations, so 8-bit is the validated setting, not a proven minimum — it's possible the corrected pipeline would now work at 4-bit too. Understanding was robust at both precisions; generation was the sensitive path.

Quantization & provenance

Quantized from the BF16 source (meituan-longcat/LongCat-Next) with per-channel symmetric int8 weights on the MoE experts and the n-gram over-embedders, and per-token dynamic int8 activations at runtime via SGLang's CUTLASS w8a8_int8 path. The MoE gate/up projections additionally use SmoothQuant (α=0.5) to migrate activation outliers into the weights before int8 — see model-smoothscale.safetensors above. The full, reproducible export scripts are in the serving repo under quantize/ (smoothquant_export.py for the SmoothQuant pass).

Hardware & serving

• Any NVIDIA GB10 system. Built for the GB10 superchip (sm_121). Validated on a DGX Spark, and expected to run on any GB10-based machine — the dependency is the chip, not the specific product. The cu130 SGLang base image is required (it's the one whose Triton compiles for sm_121); not expected to run on other GPUs.
• Optimized for headless GB10 operation at time of publishing (serve with the screen off / remote-only for maximum memory headroom).
• Context: native 128k, or 256k via YaRN (LCN_YARN=1, opt-in). MLA keeps the KV cache cheap, so long context costs little memory.
• Quickstart, the OpenAI-compatible API, and per-modality examples are in the serving repo README.

License

MIT (© Meituan). The bundled English demo voice is public-domain (LibriVox); the Chinese demo voice is Meituan's LongCat example clip (MIT). See the serving repo LICENSE.