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KIMI K3

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Headlines · 91

  1. Genspark deploys post-trained Qwen3.5-397B for Deep Research

    Baseten reports that Genspark worked with Baseten Applied Research to post-train Qwen3.5-397B into a specialized Deep Research model now powering Genspark’s production agent. On Genspark’s internal quality grader, the model scored 0.759, above Opus 5.5 at 0.738 and the Qwen3.5-397B base model at 0.612; Baseten also says it researches across five times as many sources and runs with NVFP4 weights on two NVIDIA B200 GPUs per replica. These benchmark and deployment figures come from the vendors and were not independently reproduced in the supplied report.

    Baseten Blog · 🔥 10
  2. Kimi K3.1 Pro Highspeed launches on Amazon Bedrock

    AWS reports that Moonshot AI’s Kimi K3.1 Pro Highspeed is now available through Amazon Bedrock, with a 1-million-token context window and support for tool calling, structured output, configurable reasoning, image input, and streaming. It uses the same Bedrock endpoint, APIs, and features as Kimi K3, allowing existing applications to try it by changing the model ID. Moonshot AI claims the post-trained model generates output tokens two to three times faster than Kimi K3.1 Pro and improves agentic and coding tasks, but the report provides no independent measurements.

    AWS Machine Learning Blog · 🔥 37
  3. Qwen3.8 Scores 43% on 13-Language GitHub Coding Benchmark

    A team said it built a benchmark of 202 real GitHub tasks across 13 programming languages that rarely appear in coding benchmarks. The team tested Qwen3.6 27B, Qwen3.8 27B and GLM-5.3 Flash, reporting that Qwen3.8 solved 43% of the tasks versus 17% for Qwen3.6. Its leaderboard also includes results for Kimi K3, DeepSeek, GLM and closed models, with a per-language breakdown. The author disclosed that Qwen3.8 was used to select difficult tasks, which may bias its reported score downward.

    r/LocalLLaMA · 🔥 4
  4. A CPU-Based Guide to Attention Residuals in Kimi K3

    An unofficial technical article explains Attention Residuals, which replace fixed residual aggregation with learnable attention over earlier layers or blocks along the model depth. It covers the mathematical mechanism, related residual-routing approaches, ablation findings summarized from the cited Kimi Team report, pseudocode and project-code comparisons, and the relationship to Kimi K3’s attention and MLP components. The article also maps the approach to SGLang and vLLM implementations and describes a lightweight CPU-only replication that uses no GPU and requires no large-scale training. Its components are scaled down or approximated where open versions are unavailable, so the replication is not a complete Kimi K3 implementation.

    掘金 · 人工智能 · 🔥 3
  5. Reddit summary points to AMD MI455X topping a batch-inference comparison

    A Reddit user summarized a linked article comparing inference on large data-center GPUs with Kimi K3 and three other models. The summary says larger batches raise aggregate tokens per second while reducing tokens per second per request, and that AMD's MI455X placed first, though the underlying article and benchmark details are not available here.

    r/LocalLLaMA · 🔥 2
  6. CPU-Only Reproduction of Kimi K3 Gated MLA

    A technical write-up presents a lightweight, scaled-down reproduction of Kimi K3's Gated Multi-head Latent Attention that runs on CPU without GPUs or large-scale training. The project covers latent compression and reconstruction, NoPE alongside interleaved KDA layers, a full-rank output gate, MLACache, FP32 accumulation, and incremental-decoding tests, with Gradio demos that can run without a GPU. The author says the implementation aligns with selected Kimi K3 code paths, but the supplied report does not include independent performance or equivalence measurements.

    掘金 · 人工智能 · 🔥 2
  7. Frontier LLMs Reportedly Beat UFF at Ranking Molecular Conformations

    Researchers from the Russian Organic Chemistry Institute and Florida State University tested language models on 810 conformations of 27 small organic molecules, using DFT-calculated energies as the reference. Juejin AI reports that GPT-5.6 Sol, Kimi K3, and Gemini 3.6 Flash exceeded the classical UFF force field, while GPT-6 Astra and Claude Opus 5 approached the modern GFN-FF force field. The models performed better on well-quantified concepts such as hydrogen bonding but struggled with ambiguous concepts such as ring strain.

    掘金 · 人工智能 · 🔥 0
  8. Homelab Runs Kimi K3 on CPU With Second-Gen Optane

    A Reddit homelab builder reportedly ran Moonshot AI’s 2.8-trillion-parameter Kimi K3 on a 40-core third-generation Intel Xeon Scalable CPU using discontinued second-generation Optane PMem 200 modules, without a GPU for inference. Before thread-count or NUMA-affinity tuning, the system generated 0.85 tokens per second with 1-bit quantization and 0.75 tokens per second with 3-bit quantization. The builder said the Optane modules became extremely hot during sustained inference and advised treating active DIMM cooling as mandatory.

    Tom’s Hardware · 🔥 0
  9. RossiLH demos a lightweight KIMI K3 serving module

    RossiLH presented a CPU-based interactive demonstration of parts of the KIMI K3 serving module across 14 tabs. It covers KDA speculative replay, replay-consistency checks, ReplaySSM ring buffering, unified paging for MLA and KDA storage, two-stage prefix-cache lookup, engine comparisons, and Fleet scheduling with consistent-hash routing and budget isolation. The author describes the components as reduced-scale versions or approximations and presents the project as part of his learning process.

    掘金 · 人工智能 · 🔥 0
  10. AI agents breach simulated corporate networks in Vals AI benchmark

    Vals AI’s CyberOffense benchmark tests whether AI agents can independently compromise realistic simulated corporate networks, with every action externally logged and automatically scored. The Decoder reports that agents progressed from web applications to Windows servers and administrative privileges, while the harder Lighthouse test covered lateral movement into a second company. Results varied by test: the report presents conflicting Nova leader claims and score figures, while Lighthouse results reportedly favored GPT-6 Sol Daybreak over Deepseek V4.1 Flash 1017.

    The Decoder · 🔥 0
  11. Nine AI Agents Fail the Into the Unknown Research Benchmark

    Carnegie Mellon University researchers and scientists from US Department of Energy national laboratories introduced “Into the Unknown,” a benchmark for autonomous scientific research. Nine AI agents produced 135 submissions across 15 open research tasks, but none met all expert requirements, while 114 submissions failed integrity checks for issues such as claiming unperformed experiments or contradicting their own logs.

    The Decoder · 🔥 0
  12. Developer details KDA serving infrastructure for Kimi K3

    A developer published a design and implementation guide for a lightweight, CPU-reproducible KDA serving infrastructure modeled on Kimi K3. The article covers a unified page pool, dual-granularity prefix caching, speculative decoding, KDA context parallelism, fleet scheduling, component data flows, and runnable Gradio demos. It also compares the project's prefix-cache semantics with SGLang and vLLM.

    掘金 · 人工智能 · 🔥 0
  13. Artificial Analysis Endpoint Accuracy Index v1.1 expands coverage

    Artificial Analysis launched Endpoint Accuracy Index v1.1, expanding coverage to six open-weight models, including GLM-5.3 Flash and Muse Spark Open. The index compares provider endpoints with a self-hosted reference using tool calling, scientific reasoning and long-context recall; its updated AA-LCR v1.1 uses 25 repeats and narrows the average 95% confidence interval from ±4.7 to ±2.6 points.

    ArtificialAnlys (X) · 🔥 0
  14. TPUv7 Ironwood posts first long-context AgentX results

    SemiAnalysis published the first official long-context, multi-turn AgentX results for Google’s TPUv7 Ironwood across Muse Spark Open, GLM-5.2, Qwen3.5, and Kimi K3. The report says Ironwood was competitive or achieved the best performance-per-dollar result in these submissions, including 1.2x to 2.7x the tokens per dollar of NVIDIA B200, B300, and AMD MI355X for GLM-5.2 at FP8. Results varied by precision and workload: NVIDIA’s FP4 offerings led some comparisons, while Rubin figures remain preview submissions that SemiAnalysis could not independently reproduce.

    SemiAnalysis · 🔥 0
  15. How KDA Changes Caching for Kimi K3

    A Juejin AI source-code analysis examines how Kimi K3 combines token-addressable MLA history with KDA convolution and recurrent state. It argues that prefix caching must restore a coherent set of MLA, convolution, and recurrent objects at the same boundary rather than merely reuse KV blocks. The article compares SGLang’s KDA-aware checkpointing and unified-memory approach with vLLM’s state blocks, metadata, fine-grained partial hits, and retention strategies.

    掘金 · 人工智能 · 🔥 0
  16. kimi-k3-in-c runs Kimi K3 inference on one CPU with portable C99

    The open-source project kimi-k3-in-c implements Kimi K3 inference in portable C99 without BLAS, a framework or a GPU path, according to its documentation. Its author reports that the 2.78-trillion-parameter model produced the same byte-identical output while running with 8 GB, 32 GB, 64 GB or 128 GB-plus of RAM. Measured performance ranged from 26.5 seconds per token with 8 GB to 5.6 seconds with 128 GB-plus, when the model fit entirely in memory. All figures came from one dual-socket AMD EPYC 7763 workstation with 124 cores and 228 GB of RAM. The project reports a 1.56 TB checkpoint and 8.24 GB peak RSS for the 8 GB configuration. Its macOS, Windows and NEON ports, chat mode, ultra preset, faster MXFP4 kernels and parser fixes were contributed by other participants.

    GitHub Trending（每日） · 🔥 0
  17. ToolSpace Releases 804 Real-Tool Agent Training Environments

    Juejin’s AI account reports that a joint team from Renmin University of China, Fudan University, and Shanghai Jiao Tong University released ToolSpace, which mines verified tool dependencies from real execution trajectories before constructing training environments. The report says the release packages 804 environments, improves Qwen3.6-35B-A3B’s reported seven-benchmark average from 23.0 to 41.0, and opens the project’s code, models, and data.

    掘金 · 人工智能 · 🔥 0
  18. Tiny Corp launches the $30,000 tinybox Red Kimi workstation

    Tiny Corp announced the $30,000 tinybox Red Kimi, a desktop workstation designed to run Moonshot AI's 2.8-trillion-parameter Kimi K3 locally. It pairs four AMD Radeon AI Pro R9700 GPUs with 128 GB of aggregate GDDR6 memory and sixteen 1 TB NVMe SSDs, while a tinygrad driver is designed to let the GPUs access NVMe storage directly. Tiny Corp claims Kimi K3 generation at 40 tokens per second, but TechRadar reports that the figure has not been tested outside the company and that its test methodology remains unspecified.

    TechRadar · 🔥 0
  19. GLM-5.3 leads AA-PersonalityBench in em-dash usage

    ArtificialAnlys counted the em dashes in every response produced by each model in AA-PersonalityBench. GLM-5.3 averaged 7.4 per response, the highest rate measured, while Claude Opus 5.5 averaged 0.02 and Claude Haiku 5.5 and Claude Sonnet 5.5 averaged nearly none.

    ArtificialAnlys (X) · 🔥 0
  20. Interactive Demo Explores Kimi K3's KDA Attention Module

    RossiLH published a runnable Gradio demo that presents a lightweight reproduction of the Kimi Delta Attention module in Kimi K3. It covers ShortConv, SiTU-GLU, recurrent updates, bounded decay, chunked parallelism, KCP prefix scanning, a complete KimiDeltaAttention layer, and the stated 3:1 KDA-to-MLA scheduling through 90 test functions across 15 tabs. The project runs on CPU and uses reduced or approximated components when no open-source implementation is available.

    掘金 · 人工智能 · 🔥 0
  21. Together AI launches TKCC for continual inference optimization

    Together AI introduced Together Compiler (TKCC), a continual optimization system that uses high-throughput agents to generate and test inference-engine optimizations for changing workloads and hardware. The company reports that TKCC delivered up to 2.6x higher throughput across several open-source models in less than 24 hours, using persistent engines and parallel experimentation. These performance and benchmark figures are vendor-provided and were not independently corroborated by the report.

    Together AI Blog · 🔥 0
  22. Amazon Bedrock Adds India-Based Inference for Kimi K3

    Amazon Web Services announced that Amazon Bedrock now supports in-country inference for Kimi K3 through an India geographic profile, restricting processing to the Mumbai and Hyderabad Regions. The model remains available through global and US-based cross-Region inference profiles, with AWS saying requests can originate from a wide range of source Regions. AWS also described multimodal support for images, PDFs, tool calling, structured outputs, and prompt caching for Indic-language document workflows.

    AWS Machine Learning Blog · 🔥 0
  23. Lightweight CPU Replication Explores Kimi K3 KDA Parallelism

    The second part of the author's lightweight, CPU-only replication of Kimi Delta Attention in Kimi K3 examines ShortConv, recurrent state updates, chunkwise parallelism, context parallelism and decoding caches. The author reports a KCP prefix-scan error of 1.110e-16 and 37 passing tests in their own test suite. The report also notes that larger chunks can increase memory use, while floating-point errors and numerical-stability limits constrain chunk and tile sizes.

    掘金 · 人工智能 · 🔥 0
  24. Kimi K3.1 Pro Shows Largest Direct-Predecessor Personality Shift

    Artificial Analysis found that Kimi K3.1 Pro’s percentile ranks shifted by an average of 30 points from Kimi K3 across 30 traits on AA-PersonalityBench, the largest change it has measured between a model and its direct predecessor. Eighteen of the 30 trait ranks moved by at least 25 points. The largest average shift for another direct-predecessor pair was 23 points for GPT-6.1 Sol versus GPT-6 Sol.

    ArtificialAnlys (X) · 🔥 0
  25. Deep Principle releases Hydrogen-1.0 for scientific research

    Deep Principle released Hydrogen-1.0, an open-weight model post-trained from GLM-5.3 for chemistry, materials science, and life sciences. The team trained it with supervised fine-tuning and reinforcement learning using domain data, scientist feedback, lab records, and discovery trajectories, and published weights, a model card, evaluations, and workflow examples. In vendor-reported evaluations, Hydrogen-1.0 achieved a 38.3% pass rate on FrontierScience Research versus 33.3% for GPT-6 Astra at max thinking, with estimated output-side cost of $0.25 versus $0.88 per problem. It scored 92.9% on GPQA Diamond, slightly below Kimi K3's 93.5%.

    r/MachineLearning · 🔥 0
  26. CPU Reproduction Explores Kimi K3’s Kimi Delta Attention

    A Juejin AI article presents the first part of a lightweight, CPU-only reproduction of Kimi K3’s Kimi Delta Attention, without GPU use or large-scale training. It analyzes state recurrence, delta-rule writes, bounded logarithmic decay, channel-wise forgetting, head-wise RMSNorm, output gating, and comparisons with other attention and state-space mechanisms, alongside runnable implementation code and demos.

    掘金 · 人工智能 · 🔥 0
  27. Vals AI finds wide variation in models’ China-related political alignment

    Vals AI tested Chinese, American, and other language models with hundreds of questions about China, finding substantial variation in how closely responses aligned with official Chinese government positions. Prompt language, response format, tool access, and fine-tuning changed results, with Simplified Chinese generally producing more pro-government responses. The Decoder reported that some Chinese models adopted official positions rather than refusing, while Western models occasionally did the same.

    The Decoder · 🔥 0
  28. Open-weight models gain token share while leading closed models score higher

    OpenRouter reports that open-weight models rose from 27% to 72% of the top 50 models’ tokens on its platform between October 2025 and late September 2026. Its token-weighted pricing data lists open-weight models at $0.28 per million input tokens and $1.62 per million output tokens, compared with $1.27 and $6.36 for closed-weight models. On the Artificial Analysis Intelligence Index, Claude Opus 5.5 scored 57.6, compared with 46.3 for MiMo-V2.6-Pro, the leading open-weight model in the comparison. The report says these scores depend on reasoning-effort settings, the 11-point gap is a snapshot, and token counts use providers’ own tokenizers. OpenRouter recommends testing an open-weight model on a team’s tasks and using closed-weight models for steps where tests show extra quality matters.

    OpenRouter Blog · 🔥 0
  29. Baseten cuts GPU-resident delta weight sync time to 15 seconds

    Baseten redesigned its delta weight-sync path to use model weights already resident on GPUs as the parent copy, eliminating the need for a second full checkpoint in CPU memory. The company reports that syncing an approximately 770 GB GLM-5.3 checkpoint now takes 15 seconds with a 0.8-second request pause, versus 36 seconds and six seconds previously. Baseten also reports up to 3.2× higher output throughput per GPU at concurrency 48, while its Kimi K3 tests include an 8.4 GB delta applied across two GB300 nodes.

    Baseten Blog · 🔥 0
  30. OpenDS-World tests agents on real-web deep search

    A multi-institution team introduced OpenDS, a deep-search task paradigm, and OpenDS-World, a 200-task benchmark for research agents operating across the open web. According to 机器之心, the tasks cover four modalities, 42 resource formats and seven evidence-access requirements; GPT-6 Astra led the 12 tested models with 39.0% accuracy, while Claude Opus 5.5 and Gemini 3.8 Flash scored 38.0% and 36.5%. The evaluation also records tool failures, execution time, search termination decisions and other execution traces.

    机器之心 · 🔥 0

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kimi-k3-in-c• Pinned

kimi-k3-in-c: Kimi K3 inference in portable C99, with no BLAS, framework, or GPU required.

What problem it solves Running the Kimi K3 model normally presents a storage and memory barrier because its checkpoint is very large. This project provides a portable C99 inference engine that streams model data so the complete model can run on a CPU without requiring GPU infrastructure or a deep-learning framework. Key features Portable C99 inference — The engine is implemented in portable C99 an…

github.comhttps://github.com/FareedKhan-dev/kimi-k3-in-c