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Kimi Linear combines two kinds of attention rather than replacing full attention everywhere. Most layers use Kimi Delta Attention (KDA), a recurrent linear-attention mechanism; every fourth layer uses global Multi-Head Latent Attention (MLA). Moonshot AI says this arrangement beat a full-MLA baseline in its evaluated comparisons, which used an identical training recipe. Its reported efficiency gains—up to 75% less KV-cache use and up to 6× decoding throughput at a 1-million-token context—are experimental maxima, not guarantees for other models, hardware, or workloads.
So how does Kimi Linear beat full attention? The central idea is to avoid maintaining and revisiting a full token-by-token attention state in most layers, while keeping periodic global-attention layers in the network. The tradeoff is not “attention versus no attention,” but a different balance among memory, computation, and global interactions.
What Kimi Linear is—and what “hybrid” means
Kimi Linear is a model architecture introduced by Moonshot AI. Its main change is how attention is distributed across layers: the model uses KDA in three layers for every one global-attention MLA layer. The Transformers documentation describes this pattern as full-attention MLA in every fourth layer; Moonshot’s project README calls it a 3:1 KDA-to-global-MLA ratio.
That distinction matters. Kimi Linear is not a pure linear-attention model, and it does not eliminate global attention. Instead, most layers use a recurrent update designed to reduce the cost of carrying token-level key-value information through long sequences. Periodic MLA layers retain global-attention interactions.
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Why change the attention pattern?
In conventional full attention, a token can compare with earlier tokens directly. During autoregressive generation, serving systems commonly retain keys and values from previous tokens in a KV cache so they can be reused. As context grows, that token-level state can become expensive to store and process.
Linear-attention approaches aim to summarize information through a recurrent state instead of repeatedly keeping and consulting a full set of token-level keys and values. That can change how memory and computation grow with sequence length, but the state still has to be updated and used; “linear” does not mean cost-free. Kimi Linear applies this approach to most layers and retains periodic global MLA layers rather than making the same tradeoff in every layer.
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What Kimi Delta Attention does
Gated, recurrent updates
KDA builds on Gated DeltaNet and uses a delta-rule linear-attention mechanism. In plain terms, it updates a recurrent state as each token arrives, allowing the model to carry forward a compressed representation rather than relying on a separate stored key and value for every past token in every layer.
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Chunkwise computation and the DPLR transition
The Kimi Linear paper describes a chunkwise algorithm built around a specialized Diagonal-Plus-Low-Rank (DPLR) transition-matrix formulation. The authors say this design is intended to improve hardware efficiency while remaining closer to the classical delta rule than a more general DPLR formulation. This is an implementation and algorithm choice within KDA, not evidence that every linear-attention implementation will achieve the same speed.
What Moonshot’s “beats full attention” result means
The Kimi Team’s 2025 paper, “Kimi Linear: An Expressive, Efficient Attention Architecture,” compares Kimi Linear with a full-MLA baseline using what the authors describe as an identical training recipe. The paper reports that Kimi Linear performed ahead across the tasks they evaluated in short-context, long-context, and reinforcement-learning scaling scenarios.
That is a meaningful, bounded claim: it concerns the authors’ model, baseline, training recipe, implementation, and evaluated tasks. It does not establish that Kimi Linear—or linear attention generally—will outperform every full-attention model, that it wins on every task, or that the same result will carry over to a different serving stack. The paper and official project materials inspected here do not establish an independent reproduction of the headline findings.
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Reported results and their conditions
| Measure | Moonshot’s reported result | How to read it |
|---|---|---|
| KV-cache use | Up to 75% lower, reported by the Kimi Team in its 2025 paper. | A maximum reported reduction, not a fixed saving for every context, system, or workload. |
| Decoding throughput | Up to 6× at a 1-million-token context, reported by the Kimi Team in its 2025 paper. | A reported maximum under the paper’s experiments; throughput depends on implementation and serving conditions. |
| MMLU-Pro | 51.0 at a 4k context, with speed described as similar to full attention in Moonshot AI’s repository chart caption, inspected 2026-10-07. | A repository-reported benchmark result; the caption does not make this a general speed claim. |
| RULER | 84.3 at a 128k context and a 3.98× speedup in Moonshot AI’s repository chart caption, inspected 2026-10-07. | Keep the score, context length, and cited comparison together; the figure is not a universal multiplier. |
| Time per output token (TPOT) | 6.3× faster versus MLA at a sequence length of 1M, according to Moonshot AI’s repository chart caption, inspected 2026-10-07. | TPOT is a latency measure per generated token. It is not interchangeable with an overall throughput figure. |
The repository’s benchmark chart captions are not separately dated. The figures above are therefore attributed to Moonshot AI’s repository as inspected on 2026-10-07, rather than presented as independently verified or as results from a specified later release.
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What model was released
The official Base and Instruct model cards identify the released Kimi Linear checkpoints as 48 billion total parameters, with 3 billion activated parameters, and a 1-million-token context length. Total parameters describe the overall model; activated parameters describe the portion used for a given input under the model’s design. These are checkpoint-family specifications, not a claim about every Moonshot AI product.
Moonshot AI’s current repository, inspected 2026-10-07, says the released checkpoints were trained on 5.7 trillion tokens. The repository does not date that statement, so it should be treated as an undated current repository claim rather than assigned to the paper’s 2025 publication.
How to try the released model
Moonshot AI’s repository demonstrates loading the Instruct checkpoint with Transformers and serving it with vLLM. Its Transformers example recommends Python 3.10 or later, PyTorch 2.6 or later, and fla-core 0.4.0 or later. The official Base model card also documents vLLM and SGLang usage. These are the versions and software paths shown in the inspected materials; package compatibility and current requirements can change.
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The Base model card lists the MIT license for that checkpoint. That license metadata alone does not answer every organization’s questions about deployment policy, privacy, export controls, or other obligations.
Quick Recap
What the results do—and do not—establish
- They support a specific hybrid design: KDA handles most layers, with global MLA retained every fourth layer.
- They report both quality and efficiency findings: the authors compare against full MLA under an identical training recipe and report results across short-context, long-context, and reinforcement-learning scaling evaluations, alongside cache and decoding measurements.
- They do not establish universal superiority: the comparisons are the Kimi Team’s own reported experiments, not proof that all linear-attention systems outperform all full-attention systems.
- They do not promise the headline speedup in every deployment: sequence length, model implementation, kernels, and serving setup affect realized performance.
- The artifacts are digital: the project provides a paper, code, and model checkpoints. The inspected materials do not establish a dedicated hardware requirement or a specific physical product.
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