Kimi-K3-DSpark

Model overview

**Inferact/Kimi-K3-DSpark** is an **MLA-native DSpark** draft model that accelerates Kimi-K3 on **vLLM**, served through vLLM's native `dspark` speculative method. The draft trains on **target hidden states extracted from vLLM itself** — the same engine that serves it, so the numerics it learns from are the numerics it meets at inference. TorchSpec provides the loop, streaming those hidden states from live target inference into concurrent FSDP draft training. **DSpark** = a block-diffusion backbone of 5 dense layers with non-causal attention, drafting 7 tokens in a single parallel pass, a low-rank sequential **Markov head** supplying the intra-block dependency, and a confidence head for resource-aware scheduling. Mirroring Kimi-K3's own MLA attention means draft and target share one KV layout (a compact 576-element latent per token), so the draft's pages unify with the target's cache — KV offloading and P/D disaggregation work with no separate page format.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。