China Chips, DeepSeek And Kimi’s Hidden Intersection

“K2.6 Is Our Strongest Code Model To Date,” Kimi Stated On Its Official Channel.

On the evening of April 20, Kimi officially released K2.6, an open‑source model that the company says delivers enhanced programming and Agent capabilities, arriving roughly one quarter after K2.5. Rumors circulated that DeepSeek V4 may also be released this week; if that materializes, it would mark yet another direct overlap between Kimi and DeepSeek. Beneath these surface collisions, however, lies a subtler convergence: both large‑model startups appear destined to move in tandem with domestic chip ventures.

Rewinding To March 2026, Yang Zhilin Addressed Kimi’s Technical Roadmap At Nvidia’s GTC, Observing That Many Widely Adopted Standards Are Essentially Eight Or Nine Years Old And Now Constrain Scaling. To Tackle Those Limits, Kimi Has Contributed Several Innovations To The Open‑Source Community, Including The Second‑Order Optimizer MuonClip, The Kimi Linear Architecture For More Efficient Long‑Context Processing, And Attention Residuals To Improve Layer Connectivity In Deep Neural Networks.

Yang Summarized Kimi’s Evolutionary Logic As Centered On Token Efficiency, Long‑Context Handling, And The Integration Of Agent Clusters. The Newly Released K2.6 Can Be Viewed As The Latest Milestone Along That Scaling Trajectory.

Code Remains One Of The Most Standardizable And Contested Capabilities Among Leading Models. From K2 To K2.5 And Now K2.6, Kimi Has Maintained An Approximately Quarterly Iteration Cadence. Although The Version Number Is Modest, It Suggests Additional Technical Levers May Remain Unexposed.

Kimi Reports That K2.6 Shows Marked Improvements In Long‑Range Coding, Able To Sustain Continuous Coding Sessions For Thirteen Hours And Produce Or Modify More Than 4,000 Lines Of Code. On Kimi’s Internal Evaluation Suite, Kimi Code Bench, K2.6 Outperformed K2.5 By About 20% Across A Range Of Complex End‑To‑End Tasks.

Within Agent Frameworks Such As OpenClaw And Hermes, K2.6’s Principal Gains Target API Call Accuracy And Stability Over Extended Runs—Enhancements That Reduce Execution Costs And Improve Task Completion Efficiency. Building On The “Agent Cluster” Concept Introduced In K2.5, K2.6 Expands Parallelization, Integrating Broad Search With Deep Research, Large‑Scale Document Analysis With Long‑Form Composition, And Multi‑Format Content Generation, Supporting Up To 300 Sub‑Agents Working In Parallel Across As Many As 4,000 Collaborative Steps.

Summarized Concisely, K2.6’s Key Advances Include Evolved Code And Long‑Horizon Task Capabilities, Enhanced Agent‑Cluster Functionality, And Optimizations For Compatibility With Mainstream Agent Frameworks.

Among These Features, The Agent‑Cluster Capability Stands Out For Its Practical Value: It Materializes The Explosive Potential Of Parallel Computation, Enabling Agents To Drive Tangible Productivity Gains Rather Than Merely Describing Possibilities. Early Users Have Reported That K2.5 Already Served Effectively As A Research Assistant, And The Multi‑Agent Support Provided By The Official Release Was Viewed As A Meaningful Step Beyond Earlier, More Toy‑Like Domestic Agent Implementations.

Kimi’s Roadmap Continues To Introduce Technical Narratives That Attract Industry Attention. In Mid‑March, Kimi Published The Attention Residuals Paper, Proposing The Use Of Attention Mechanisms To Reconfigure Residual Connections; Elon Musk Publicly Praised The Work As An Impressive Breakthrough. More Recently, Kimi Released A Paper Titled “Prefill‑As‑A‑Service: KVCache Of Next‑Generation Models Could Go Cross‑Datacenter,” Which Explores Architectural Separation Between Prefill And Decode Phases.

The Prefill‑Decode Separation Is Not New In Principle: Prefill Is Compute‑Intensive While Decode Depends On Memory Bandwidth, With KV Cache Read/Write Patterns Driving Heavy Bandwidth Use. The Objective Is To Decouple Compute‑Heavy And Bandwidth‑Heavy Tasks To Improve Utilization And Throughput, Thereby Reducing Costs And Increasing Efficiency. A Practical Constraint For Such An Approach Is The Need For High‑Speed RDMA Networks Within The Same Data Center.

Kimi’s Prefill‑As‑A‑Service Proposal Focuses On Reducing KV Cache Size Through A Hybrid Model (Kimi Linear) And Fully Decoupling Prefill And Decode Across Heterogeneous Clusters. The Paper Describes A Test Setup Where A Dedicated Prefill Cluster Uses 32 High‑Compute H200 Cards, While A Local PD Decode Cluster Uses H20 GPUs Interconnected Via RDMA; The Two Clusters Are Linked Over VPC With Approximately 100Gbps Cross‑Cluster Bandwidth. The Benchmark Model Was A 1‑Trillion‑Parameter Kimi Linear Hybrid‑Attention Model.

Measured Results Indicate That The Cross‑Data‑Center PrfaaS‑PD Configuration Achieved A 54% Throughput Increase Compared With A 96‑Card H20 PD‑Only Cluster, While P90 TTFT (The Time For 90% Of Users To See The First Token) Fell From 9.73 Seconds To 3.51 Seconds, A Reduction Of 64%. Cross‑Data‑Center KV Cache Transfers Consumed Only 13% Of The Total 100Gbps Bandwidth.

To Demonstrate The Hybrid Architecture’s Advantage, The Paper Reports A Benchmark Using Eight H200 Cards And The SGLang v0.5.9 Inference Framework Across Several Mainstream Models At A 32K Context Length. Under These Conditions, A Mixed‑Attention MiMo‑V2‑Flash Model Required Only 4.66Gbps Of KV Throughput, Whereas A Dense‑Attention MiniMax‑M2.5 Model Consumed 59.93Gbps, Illustrating That Hybrid Attention Can Reduce KV Cache Transfer Demands To Levels Manageable By Standard Ethernet.

“Cross‑Data‑Center Plus Heterogeneous Hardware Unlocks Significant Potential To Lower Per‑Token Costs,” Kimi Stated On Its Official Channel.

On Token Cost Reduction, Observers Note That Both Model And Hardware Optimizations Are Necessary. Professor Hu Yanping Of Shanghai University Of Finance And Economics Emphasized That Cost Reduction Depends On Compute Supply Efficiency, Cross‑Generational Model Improvements, Continued Advances In Intelligent Paradigms, And Workflow And Scenario Integration—Not On Any Single Vendor.

In The Prefill‑As‑A‑Service Paper, Many Readers Focused On The Cross‑Data‑Center Narrative And Overlooked The Role Of Heterogeneous Hardware. It Is Important To Recognize That H200 And H20 Share The Hopper Architecture; The Paper’s Heterogeneity Refers To Differences In Bandwidth And Compute Characteristics. The Implication Is That High‑Compute Domestic Cards Could Serve Prefill, While Bandwidth‑Optimized Domestic Cards Could Handle Decode, Or That Domestic And Overseas Cards Could Be Mixed To Achieve Cost And Efficiency Gains.

Viewed This Way, Kimi Has Opened A Door For Domestic Chips In Large‑Model Inference. Industry Participants Note That To Capture The Traffic Benefits From A PrfaaS Approach, The Ecosystem Challenge Remains Significant. Over Recent Years, Chinese Large Models Have Often Been Constrained By Ecosystem Limitations That Kept Them From Fully Leveraging Domestic Compute. Another Practical Detail Is That Products Like H20 Have Been Unavailable For A Year, Leaving Domestic Inference Chips As The Only Short‑Term Option.

As Inference Demand Surges, The Ecosystem Constraint May Become Secondary To Supply Realities—Chinese Models’ Dependence On Domestic Compute Could Shift From Optional To Mandatory. For That Reason, Many Analysts Expect DeepSeek V4 To Be Adapted For Domestic Hardware. While DeepSeek V4 Has Not Yet Arrived, Kimi Has Already Demonstrated A Viable Path For Integrating Chinese Models With Chinese Chips.

Recall Jensen Huang’s Reaction On The Latest The Dwarkesh Podcast When Asked About Banning Chip Exports To China: He Argued That Chips Are Not Uranium Enrichment, And That Export Controls Would Not Prevent China From Advancing Through Domestic Stacking. Why Huang Made That Point Is Now Becoming Clear: The Next Phase For DeepSeek And Kimi Will Provide A Practical Answer.