kimi-k2.6-eagle3-mla is an Eagle3 MTP draft model with MLA (Multi-Latent Attention) for accelerating inference of Kimi-K2.6, trained with TorchSpec — an online speculative decoding training framework that runs FSDP training and inference concurrently. If you find this draft model useful, please give our project TorchSpec a star on GitHub. Compared with an MHA draft model, the MLA variant is a better fit for Kimi-K2.6 deployment: - Uses less KV cache, which reduces serving memory pressure. - Matches Kimi-K2.6's MLA architecture, so it fits more naturally into the inference engine's KV-cache handling under different serving scenarios such as PD-Disaggregation.

Text generation

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kimi-k3-dflash2

lightseekorg

Draft model for speculative decoding on `moonshotai/Kimi-K3`, served with TokenSpeed (`--speculative-algorithm DFLASH`). Acceptance length `acc_len = 1 + accepted/steps` at 7 speculative proposals, served with TokenSpeed DFLASH on two GB300 nodes (8 GPUs, TP8), MLA target and drafter. Target is `moonshotai/Kimi-K3` with the serving flags shown at the bottom of this card. Production sampling only: `temperature=1.0`, `top_p=0.95`, reasoning effort `max`, no `top_k`. RULER multi-key needle retrieval over natural-text paragraphs, with only the input length varying. `retrieval` is exact match on the needle value, recorded so that acceptance cannot be credited to a model that has stopped reading its context.

Category pending

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kimi-k3-eagle3.1-mla

lightseekorg

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation

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kimi-k3-eagle3-mla

lightseekorg

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Text generation

Input Text→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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