---
license: apache-2.0
base_model: Kwaipilot/KAT-Coder-V2.5-Dev
tags:
- nvfp4
- compressed-tensors
- gb10
- veloGB10
- code
- moe
---

# KAT-Coder-V2.5-Dev — NVFP4 for veloGB10 (NVIDIA DGX Spark / GB10)

> **Original model:** [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev) by the KwaiKAT team,
> [Apache-2.0](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev/blob/main/LICENSE). This artifact contains
> **modified weights** (NVFP4/FP8 quantization); the original model is © its authors.

NVFP4/FP8-quantized variant of **KAT-Coder-V2.5-Dev**. **This quantized artifact exists first and foremost to run with the
[veloGB10](https://github.com/sf-stav/veloGB10) inference engine** (`gb10_inference`) on NVIDIA
DGX Spark / GB10 systems — it is produced, validated, and tuned for veloGB10's kernels.
The weights use the standard HuggingFace `compressed-tensors` layout (`nvfp4-pack-quantized`),
so this artifact can be used **for any purpose, with any framework that reads the format**. It
is in particular fit and *proven* to work with veloGB10 — greedy-lossless speculative decoding
(where applicable), two-node TP=2 serving, and the engine's full correctness test suite all pass
on this artifact.

## Specifications

| | |
|---|---|
| Base model | [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev) |
| Architecture | MoE hybrid (40 GatedDeltaNet/full-GQA layers), hidden 2048, 16 Q / 2 KV heads |
| Attention | hybrid GatedDeltaNet (linear attention, fixed-size recurrent state) + periodic full GQA |
| Experts | 256 routed experts, 8 active per token, plus shared expert |
| MTP head | none in this checkpoint — speculative decoding uses n-gram draft or future MTP heads |
| Context | 262,144 tokens |
| Vocabulary | 248,320 |
| Quantization | `nvfp4-mixed` — all GEMMs NVFP4 except GDN in/out projections (FP8); router kept in bf16 |
| Format | `nvfp4-pack-quantized` (compressed-tensors compatible) |
| On-disk size | ~19 GB |

## Running with veloGB10

Full instructions: **[veloGB10 README](https://github.com/sf-stav/veloGB10)** (build or download a release binary, then).
The binary loads its PTX kernel artifacts from the current directory — run it from wherever the
binary + `*.ptx` live.

**Single user — maximum speed:**

```bash
gb10_inference --server --model-dir=/path/to/Kwaipilot-KAT-Coder-V2.5-Dev-NVFP4-MIXED --port=9000 \
  --max-seq-len=32768 --max-batch=1 --max-tokens=4096 \
  --prefix-cache=on --mtp=off --default-presence-penalty 1.5
```

**Four concurrent users — maximum aggregate throughput:**

```bash
gb10_inference --server --model-dir=/path/to/Kwaipilot-KAT-Coder-V2.5-Dev-NVFP4-MIXED --port=9000 \
  --max-seq-len=32768 --max-batch=4 --max-tokens=4096 \
  --prefix-cache=on --default-presence-penalty 1.5
```

**Two-node TP=2 (maximum speed and capacity):**

On the second node (no model copy, no configuration — the head ships weights, config, and
calibration at sync):

```bash
gb10_inference --node --port 29500
```

On the head node:

```bash
GB10_TP_SHARD_MIXERS=1 gb10_inference --server --model-dir=/path/to/Kwaipilot-KAT-Coder-V2.5-Dev-NVFP4-MIXED \
  --tp --nodes <peer-ip>:29500 --port 9000 \
  --max-seq-len=32768 --max-batch=1 --max-tokens=4096 \
  --prefix-cache=on --default-presence-penalty 1.5
```

## Measured on veloGB10 (greedy, NVFP4)

*Pending — single-node and TP=2 throughput numbers are being gathered and will be added here
once the full gate suite completes. See the [veloGB10 README](https://github.com/sf-stav/veloGB10)
for the latest measured tables across the supported model family.*

## License & credits

- **Original model:** [Kwaipilot/KAT-Coder-V2.5-Dev](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev) — © KwaiKAT team,
  [Apache License 2.0](https://huggingface.co/Kwaipilot/KAT-Coder-V2.5-Dev/blob/main/LICENSE).
- **This artifact:** quantized from the original with veloGB10's offline quantizer
  (`nvfp4-mixed`). Weights are modified relative to the original; tokenizer and chat template are
  unchanged from the original model.
- **Engine:** [veloGB10](https://github.com/sf-stav/veloGB10) (Apache-2.0).