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KIMI K3

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Headlines · 91

  1. Together AI launches Together Post-Training service for RL and SFT

    Together AI introduced Together Post-Training, a service for building custom models on its platform through reinforcement learning from environment rewards and supervised fine-tuning on supervision data. The company says users define the entire training loop while Together co-optimizes the underlying systems and infrastructure, with support for LoRA training (including multiple adapters sharing GPU capacity) and full-weight experiments for select models. It launches with open-weight models including Muse Spark Open, GLM 5.3, Kimi K3 and Qwen 3.8-27B, priced at a reduced $9 per GPU-hour of B200 usage for the first eight weeks.

    Together AI Blog · 🔥 0
  2. OpenVLHarness pairs tool use with multimodal memory, lifts scores 9.7–12.6 points

    An arXiv paper (arXiv:2610.26860, submitted 19 October 2026) by Yu Zhou and 10 co-authors introduces OpenVLHarness, a multimodal reasoning harness built around a capability layer and persistent multimodal memory. The capability layer combines task-level tool interfaces with evidence rendering, converting outputs from heterogeneous backends into model-readable observations, while persistent memory keeps the corresponding images, structured data and web references as addressable artifacts; a coding interface executes generated programs over that stored data. The authors report that across 22 benchmarks with eight open and proprietary backbones, OpenVLHarness improves average scores by 9.7–12.6 points over matched no-tool baselines, and that with GPT-6 Luna at medium reasoning effort it beats the base model, Codex and Visual Sketchpad at maximum effort by 7.7–8.5 points at up to 37% of their model-token cost.

    Hugging Face · Papers · 🔥 0
  3. Meta releases Muse Spark Open, the strongest US open-weight model, analyst says

    Interconnects argues that Meta's Muse Spark Open, released this week, is now the strongest American-made open-weight model by a wide margin. According to the Hugging Face model card cited in the piece, it has 816B total and 42B active parameters, a 1M-token context window, native multimodality and is built for coding and agentic work. The analysis places its performance above GLM 5.2 and near DeepSeek V4.1 Flash, but behind GLM 5.3 and Kimi K3, and notes that Meta also unveiled the closed frontier models Muse Wonder and Muse Wonder Full days earlier. It says the gap between the best US and Chinese open models has been roughly halved.

    Interconnects · 🔥 0
  4. DeepSeek releases V4.1-Flash-1017 open-weight model

    DeepSeek released DeepSeek-V4.1-Flash-1017, which the company described as a Pareto improvement in performance and cost, now live on its website, app and API, with a testing account on X saying it is also on HuggingFace. 36Kr reports the base architecture is unchanged and the update focuses on capability iteration: DeepSWE v1.1 Pass@1 rose from 74.2 to 77.0, above Claude Opus 5 at 74.0 and GPT-5.6 Sol at 73.0, and Terminal-Bench 4.0 went from 31.2 to 42.3, while HLE scored 42.6 versus 56.3 for Opus 5. The report adds that API access and pricing are unchanged, with savings coming from shorter task runtimes and outputs, and that a Nebius employee's independent test passed all 30 runs versus 24 of 30 for the earlier version, at about 4.5 cents and roughly 550,000 tokens per task.

    testingcatalog (X) · 🔥 0
  5. Chinese GPU makers race on supernodes as buyers weigh cost and ecosystem

    At the 26th China International Industry Expo, where the integrated-circuit zone debuted as a standalone exhibition, domestic GPU vendors focused on rack-scale "supernodes": MetaX showed its 64-GPU fully interconnected Xijing S600 plus a 16-GPU S600 Lite, Enflame showed the Yunsui ESL64-O built with ZTE, and Iluvatar CoreX packed 144 GPUs and 18 CPUs into one 44U rack, saying the TianGai 300 IXL144 reaches 100-nanosecond single-hop latency and a 15% effective bandwidth gain in certain MoE All-to-All scenarios (all vendor claims). Citing an IDC report via Reuters, the article says China's data-center AI accelerator shipments reached 2.008 million units in the first half of 2026, with Nvidia at 678,000 (34%) and Huawei at 572,000 (28%), while China GPU shipments fell 26% year on year to 991,000 units and other AI accelerator types rose nearly 80% to 1.016 million. The report argues that beyond performance and price, stable supply, software ecosystem maturity and the ability to bring real customers and revenue to compute centers will shape commercialization, with vendors ultimately answering how to lower customers' total cost of ownership.

    36氪 人工智能 · 🔥 0
  6. Contested essay claims neural networks are far more readable than assumed

    On October 15, 2026, Goodfire researcher and former OpenAI interpretability scientist Nick Cammarata published a long essay, “Neural Networks Are Already Clean,” arguing that large models’ neurons are far more legible than the field had assumed and that earlier readings of “superposition” may have been mistaken. The essay offers preliminary estimates that in Qwen3-32B about 90% of neuron poles have one main job and 2–4% remain unexplained, describes attribution graphs for a trillion-parameter Kimi K3 model, and reinterprets the classic InceptionV1 cat-and-car neuron as detecting V-shaped geometry. The claims have not been peer-reviewed or independently replicated, Google DeepMind researcher Neel Nanda has warned against overhyping them, and MIT Technology Review Chinese notes that the superposition hypothesis is not yet refuted.

    创业邦 科技 · 🔥 0
  7. vLLM Uses Coding Agents to Optimize Kimi K3 Inference on GB300

    The vLLM team said it used agent-assisted development to build a unified CuTe DSL decode path for Kimi K3 on NVIDIA GB300 systems, covering attention, MoE and communication across 8 or 16 GPUs. The team reported more than 840 tokens/s at batch size 1 on 16 GPUs with DSpark, 1.5–2× higher decode throughput in several tested configurations and up to 1.8× higher P90 interactivity on AgentX. SemiAnalysis separately reported 423 tok/s/user on GB300 NVL72, a 1.68× speedup, while the figures depend on different configurations and measurement methods.

    vLLM Blog · 🔥 0
  8. vLLM: Kimi K3 on AMD MI355X tops NVIDIA HGX B300 throughput per GPU

    A vLLM blog post details the engineering behind serving Kimi K3 on an eight-GPU AMD Instinct MI355X system with vLLM, covering cache reuse of conversation state, eliminating duplicate MoE work across GPUs, fusing memory traffic, an AMD AITER assembly path that speeds long-context MLA decoding, and parallelized KDA prefill and speculative decoding. Citing a SemiAnalysis InferenceX snapshot from October 9, 2026, it reports about 7,567 total tokens/s/GPU at 50 tokens/s/user P90 interactivity versus 6,083 for NVIDIA HGX B300, a 24.4% advantage. Using modeled TCO of $1.50 versus $2.26 per GPU-hour, it claims 87.4% more tokens per dollar; the figures come from a vendor-adjacent source and modeled cost assumptions, with no independent replication reported.

    vLLM Blog · 🔥 0
  9. 17-year-old Kimi architecture co-author Nathan Chen leaves Moonshot AI for Bay Area

    On October 15, 17-year-old Nathan Chen announced on X that he had left Moonshot AI's Kimi team, calling the past year the best of his life so far. He joined in November 2025 as a 16-year-old intern and is a co-first author of the Attention Residuals paper, work that reports say is used in the Kimi K3 architecture. Anthropic researcher Sholto Douglas invited him to San Francisco in the comments, and Chen replied that he is heading to the Bay Area, but when asked whether he was joining Anthropic he answered "no"; his next employer, role and start date remain unknown. DoNews headlined the move as an Anthropic poaching, which conflicts with Chen's own denial.

    36氪 人工智能 · 🔥 0
  10. Meta releases Muse Spark Open, an 816B open-weight multimodal model

    Meta released the weights of Muse Spark Open on 15 October, an open-weight mixture-of-experts model with about 816 billion total and 42 billion active parameters and a context window of up to one million tokens. It accepts text, images, audio and video as input and produces text, and Meta trained it for agentic coding, tool use and long-horizon tasks. Meta, which calls it the most capable US open-weight model to date, worked with Apple so the model can run locally on two M5 Ultra Mac Studio desktops via MLX; it is also offered through third-party APIs including Together AI, Baseten, Fireworks AI, Groq, Modal, Nebius and OpenRouter, with weights on Hugging Face under the Muse Model License. Arena.ai ranked it the top US open model on its Code Arena: WebDev leaderboard at 1511 points and 13th among open models overall, while an Artificial Analysis score of 39 cited in community discussion placed it behind GLM 5.3 and Kimi K3.

    Hugging Face · 🔥 0
  11. Artemis launches Orion-1 security model, claiming edge on vendor benchmark

    US startup Artemis Security has launched Orion-1, a model it says can investigate and respond to cyberattacks on corporate networks on its own, connecting logs from auth services, cloud setups, endpoints, SaaS apps and network gear. On Artemis' own Decision-Grade Readiness benchmark, Orion-1 scored 80.8 in attack reconstruction versus 58.3 for the next-best model, ahead of Claude Opus 5.5 and GPT-6 Sol; the comparison omits Claude Mythos 5 and GPT-6 Astra. Orion-1 is in private preview for select customers, with per-action limits and human approval by default for high-impact actions; Artemis calls it a "Frontier Foundation Model" but only details fine-tuning, leaving its base model unclear.

    The Decoder · 🔥 0
  12. DeepRSI debuts: Claude Fable 5.1 tops leaderboard, Kimi K3 third

    Hillclimb, an AI startup backed by Jeff Dean, released the DeepRSI benchmark and its first results, testing models' recursive self-improvement ability on 15 tasks drawn from real machine learning research — covering training-recipe optimization, GPU kernel acceleration, estimator accuracy and vector search. Models ran for 48 hours without network access in a shared Pi Agent framework at maximum reasoning effort, proposing approaches, writing code, running experiments and iterating. Claude Fable 5.1 led with an Elo of 1548, ahead of GPT-6 Astra at 1232, while Moonshot AI's Kimi K3 placed third at 820 as the top open-weight model, ahead of Grok 4.7 and Gemini 3.8 Flash. The report notes the benchmark is still early: 15 tasks, five model configurations, two runs per model, with goals, environment and scoring still set by humans.

    36氪 人工智能 · 🔥 0
  13. Jefferies and Bain warn cheap Chinese and open models threaten hyperscaler AI returns

    TechNews cites new analyses from investment bank Jefferies and consultancy Bain warning that the rapid rise of low-cost Chinese and open-source models is pushing down large-language-model token prices, putting US cloud providers' trillion-dollar data-center investments at risk of not being recovered. Jefferies says about half of roughly $2.3 trillion in contracted backlog at Microsoft, Alphabet, Amazon and Oracle comes from OpenAI and Anthropic, while Chinese models now process 76.12 trillion tokens per week, up more than 17-fold from 4.37 trillion before the end of April, versus 14.2 trillion for the top four US providers. The report also cites a Jefferies estimate that Moonshot AI's open-source Kimi K3 reaches about 95% of Anthropic's flagship Claude Fable 5, and Bain's estimate that the AI industry must generate $6 trillion in annual revenue by 2031, leaving a $4.2 trillion gap against roughly $1.8 trillion today.

    科技新报 · 🔥 0
  14. Moonshot AI reported at $50B valuation as it moves toward a 2027 Hong Kong IPO

    Citing people familiar with the matter, Bloomberg reported on October 6 that Moonshot AI has closed a final funding round at a valuation of about $50 billion and plans to list in Hong Kong in the first quarter of 2027, according to articles republished by 36Kr and TMTPost from Bohu Finance. That is about a month after Moonshot was reported to have filed a confidential A1 listing application with the Hong Kong Stock Exchange. The articles say K3, released in July, topped the Hugging Face trending list within 30 minutes, and that annualized recurring revenue rose from $100 million in March to $1 billion in August, with Bloomberg's sources pointing to $2 billion by year-end. They also list September commercialization moves, including the "Moon Landing" partner program, the full rollout of K2.8 Preview, K3 on Amazon Bedrock, and the release of Kimi Code Desktop and a financial-industry solution. The funding and IPO details rest on unnamed sources, have not been confirmed by the company, and contrast with founder Yang Zhilin's late-2025 internal letter saying a listing was not an immediate priority.

    36氪 人工智能 · 🔥 0
  15. Jensen Huang: Chinese open models like Kimi are 'good enough,' backs open source

    Speaking at a forum hosted by DBS Bank, NVIDIA CEO Jensen Huang said Chinese AI models including DeepSeek, Kimi and GLM are "already good enough." He described himself as a firm supporter of open source, calling it the best way to drive efficiency gains, and said the market will decide which models best fit different use cases. Huang also rejected the framing of AI competition as "a race with a finish line," saying both the US and China will be long-term players, and argued that cheaper, more open models will let more people and companies benefit from AI, increasing demand for NVIDIA chips, data centers and compute. The report says he argued the market first misread DeepSeek's impact and that Wall Street is now misreading Kimi's, and that quantum computing could reach mainstream adoption within three to five years.

    cnBeta · 🔥 0
  16. Arena weekly: Claude Opus 5.5 Max debuts first on agent leaderboard

    Arena published its Week 41 AI model leaderboard (Oct 5–11, 2026), where Anthropic's Claude Opus 5.5 Max debuted at No. 1 on the agent board with a 15.53% net improvement, a 17.29% task-confirmation success rate and a 0.37% tool-hallucination rate. On the overall board Gemini 4 Argon (High) stayed first at 1525 ELO with Anthropic models filling the rest of the top four, while new entries gpt-5.5 and gemini-3.5-flash-medium placed 29th and 30th. Chinese models sat in the upper-middle tier and moved little: Moonshot's kimi-k3-max 16th overall, Alibaba's qwen3.8-max 22nd, Xiaomi's mimo-v2.6-pro 26th and Z.ai's glm-5.3-max 27th, with glm-5.3-flash the highest Chinese model on the coding board at 27th. Rankings use anonymous blind-vote ELO scores (a percentage signal for the agent board) and reflect user preference rather than absolute capability.

    IT之家 AI · 🔥 0
  17. AirLLM claims 2.8T-parameter Kimi K3 runs on a single 4GB GPU, adds low-VRAM training

    AirLLM is an open-source inference and training library that streams weights one layer at a time, keeping only a single layer resident on the GPU to cut VRAM use sharply. According to its repository, it runs 70B models on a single 4GB card without quantization, distillation or pruning, and reports running Kimi K3 (2.8T) in 3.72GB, Qwen3.8-Flash-Next (125B) in about 5.95GB and DeepSeek-V3 (671B) in roughly 12GB. Recent updates also add small-VRAM training, fine-tuning Qwen3.8-Flash-Next (125B) under 6GB and Qwen3.8-27B at about 2GB. All VRAM and training figures are the project's own reported measurements and have not been independently verified.

    GitHub Trending（每日） · 🔥 0
  18. Washington Post probe: US AI industry leans heavily on Chinese models and parts

    A Washington Post investigation reported that the US AI industry is deeply intertwined with Chinese supply chains at the model, hardware and component levels, with US companies including DoorDash and Airbnb using Moonshot AI's Kimi K3 and other Chinese open-weight models for everyday tasks because they cost far less than closed models from Anthropic and OpenAI; when the Trump administration weighed a national-security ban on Chinese AI in July, a coalition of nearly 300 US companies publicly objected, the report said. It said Congress began examining US firms' use of Chinese AI models in April, and that the FBI, NSA and a DHS cyber agency issued a joint advisory last month alleging Moonshot AI, DeepSeek, Alibaba, MiniMax, StepFun and Zhipu trained on US models without authorization and breached US companies' terms of service — allegations attributed to US agencies. The report also cited Epoch AI in putting six leading Chinese AI firms' disclosed annual revenue at roughly 10% of Anthropic and OpenAI combined, and a Morgan Stanley estimate that making Tesla's Optimus 2 with Chinese suppliers costs about $46,000 in materials versus nearly three times that, $132,000, without them.

    cnBeta · 🔥 0
  19. Mistral releases trillion-parameter Large 4, the best model outside the US and China

    European AI lab Mistral has released Large 4 (also called ML4 and nicknamed "Le Chonk" on social media), a trillion-parameter model. Testing lab Artificial Analysis scored it 38 on its overall intelligence index, according to Tom's Hardware, leaving it behind Chinese models such as Kimi K3 (max), GLM-5.3-Flash and MiMo-V2.6.Pro, though it performs strongly on cybersecurity tasks. Tom's Hardware reported that independent tests rank ML4 as the best AI model outside the U.S. and China, and said Mistral expects the exact weights to be released before the end of October.

    Tom’s Hardware · 🔥 0
  20. PPT Master: open-source AI workflow builds natively editable PowerPoint decks

    PPT Master is an open-source Python workflow by developer Hugo He that runs inside any agent-capable AI tool (such as Claude Code or Codex CLI) and turns PDFs, DOCX files, web pages or a bare topic into a natively editable PowerPoint deck — including slide masters and layouts, native shapes and connectors, data-backed charts and tables, transitions, animations and narration from speaker notes, plus support for a user's own .pptx templates. The project bills itself as free and open source with no PPT subscription, with everything except model communication running on the user's machine to avoid platform lock-in. The author recommends a large-context model (Kimi K3 or Claude) paired with an AI image generator such as gpt-image-2 or gemini-nano-banana-2.1, and warns that a single pass will not produce a flawless deck; the page also carries sponsor promotions for Kimi, PackyCode, APIKEY.FAN, RunAPI and APIMart.

    GitHub Trending · Python · 🔥 0
  21. AWS recaps September Bedrock, AgentCore and Strands updates

    An AWS Machine Learning Blog recap says Amazon Bedrock added several capabilities for AI builders in September: Amazon Bedrock Managed Agents powered by OpenAI is now in public preview, and the AgentCore runtime has lower cold-start latency and scales sessions to zero when idle. Strands introduced an open source agent harness that AWS says matches popular harnesses on accuracy while using 28 percent fewer tokens, plus Strands Decider 2B, an open source 2B-parameter decision model that returns answers locally in about 115ms. Bedrock also lists OpenAI's GPT-6 Astra, Sol and Luna, along with Claude Fable 5.1, Claude Opus 5.5, Claude Sonnet 5.5, Moonshot AI's Kimi K3 and xAI's Grok 4.6 and 4.7, and added native ServiceNow, Salesforce and Zendesk connectors to its managed knowledge base.

    AWS Machine Learning Blog · 🔥 0
  22. Blog SGLang and Miles on NVIDIA Vera Rubin We had early access to two NVIDIA Vera Rubin nodes (8 GPUs) and used them to bring SGLang and Miles up on the platform: SGLang for inference and Miles for RL training. SGLang & Miles Team October 9, 2026

    On this page Summary Inference: Serving Kimi K3 NVFP4 Attention MoE and communication kernels KDA RL training: Miles on NVIDIA Vera Rubin Agentic RL on the Vera CPU What's Next Acknowledgements Summary We had early access to two NVIDIA Vera Rubin nodes (8 GPUs) and used them to bring SGLang and Miles up on the platform: SGLang for inference and Miles for RL training. On the inference side, we tuned SGLang for Kimi K3 NVFP4: this post covers what we changed in the attention, MoE/communication and KDA kernels, and how much each change bought. On the training side, Miles, which uses SG

    LMSYS Blog · 🔥 0
  23. PaperBenchX: best AI agent fully reproduces only 14% of papers

    UniPat AI's PaperBenchX benchmark gives 10 frontier agent configurations a published paper and asks them to re-run the key experiments in real scientific software, across 93 tasks covering 12 research directions. According to the team's published results, 70 of the 93 tasks were not fully reproduced by any configuration, and the top performer, GPT-6 Astra, fully cleared only 14% (13 tasks), while Chinese models clustered around 7% — roughly half that. Scoring separates modeling, execution and scientific verification across 3,168 scorable criteria, with a 4–24 hour per-task budget and outputs wiped and re-run in an isolated environment before grading.

    品玩 实时要闻 · 🔥 0
  24. Goodfire launches inside-out monitors for AI agents

    Interpretability startup Goodfire released "inside-out" agent monitors that read a model's internal activations via probes rather than having a second AI reread every output, and they are available to Baseten customers. In Goodfire's tests on Kimi K3, monitoring about 1,500 sessions cost roughly $51, versus about $233 for a cheaper model checking every step and about $10,000 for a top-tier one; the probes caught 94% of malicious hacking sessions while flagging 8.7% of harmless ones for a second look, and running four probes added under 2% to response start time. Customers choose which risks to monitor, including offensive hacking, chemical and biological weapons misuse and reward hacking, and set the response: log, send for human review, or refuse the request.

    TechCrunch AI · 🔥 0
  25. Step 5 Preview free for a week on OpenCode, Cline, Kilo and Nous Portal

    StepFun said its flagship model Step 5 Preview is free for one week in OpenCode, Cline, Kilo and Nous Research's Nous Portal for agentic and professional work. Material relayed by StepFun describes the model as a 600B-total, 27B-active MoE with a 1M-token context window and vision, and the OpenCode post also lists zero data retention. Nous Research is quoted as saying Step 5 Preview scored 33.89 on the Hermes Index, matching GPT-6 Luna, while Cline says it scores ahead of Kimi K3 and GLM-5.3 on DeepSWE; these benchmark comparisons are partner or vendor claims that have not been independently verified.

    阶跃星辰 StepFun · 🔥 0
  26. Anthropic launches Claude Haiku 5.5 at $0.10 per million input tokens

    Anthropic released Claude Haiku 5.5 on October 7, describing it as the fastest, cheapest and most capable model in its Haiku small-model line. For requests up to 100,000 tokens, it costs $0.10 per million input tokens and $0.50 per million output tokens, rising to $0.50 and $2.50 above that threshold; Anthropic says it costs about 75% less to run on average than Haiku 4.5. Anthropic-reported figures show OSWorld 2.1 offline subset rising from 15.7% to 72.4% and GDPval-AA v2.1 at 1620, while Terminal-Bench 4.0 is reported at either 39.2% or 33% across sources, still well below Sonnet 5.5. Haiku 5.5 is the first Haiku model with adjustable effort settings and is positioned as a subagent for Opus 5.5 and Sonnet 5.5; Anthropic also cut Sonnet 5.5 cache-read pricing from $0.20 to $0.10 per million tokens.

    IT之家 AI · 🔥 0
  27. Interconnects: open-weight cyber-risk discourse is broken

    An Interconnects opinion piece argues the public debate over open-weight models' cyber risk has gone off the rails: Anthropic's report on GLM-5.3 as an offensive cyber tool is largely reasonable technically, but ducks cross-cutting questions such as what happens if open models are banned and why Chinese labs consider their models safe to release. The author notes that documented cyberattacks so far mostly involve closed models, so "open dangerous, closed safe" may really be "open unsafe, closed unsafe," and says Chinese companies must register major model releases with the government along with evaluations, though it is unclear whether those cover cyber or bio risks. He adds that GLM-5.3, not Claude Mythos, is the model that crossed the capability threshold, and that more than a month after its weights were released there is little public evidence of a step change in harm.

    Interconnects · 🔥 0
  28. Mistral launches Mistral Large 4 preview, a 1T-parameter open-weight model

    Mistral AI has opened a public preview of Mistral Large 4 (nicknamed Le Chonk), a 1-trillion-parameter natively multimodal open-weight model with roughly 49 billion active parameters; the weights are due by the end of the month. Mistral claims state-of-the-art open-model performance on enterprise workloads such as cybersecurity, finance and law, and says it beats even closed frontier models on visual grounding — though it notes that some cyber-test leads come from Claude and GPT-6 refusing the tasks. Independent measurement is more sobering: Artificial Analysis's Intelligence Index puts ML4 at 38 points, a big jump from Mistral Large 3's 9, but well behind leader Claude Opus 5.5 (Max) at 58.

    Mistral AI News · 🔥 0
  29. vLLM v0.31.0 expands serving, speculation, and model support

    vLLM v0.31.0 adds broad serving and model-support updates, including DeepSeek-V4.1-Flash optimizations, Model Runner V2 speculative decoding, larger-scale expert parallelism, and improved multimodal support. It also introduces the `vllm preload` weight-cache daemon for faster engine restarts and experimental CRIU-based initialized-engine snapshots, alongside several security and breaking changes.

    vLLM Releases · 🔥 0
  30. Reflection AI launches Beam, a 501B open-weight model

    Reflection AI unveiled Beam on October 5, its first open-weight model, aimed at coding, reasoning and agentic workloads. Beam is a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active, pretrained on 23.8 trillion tokens with a 1 million-token context window. The company says it approaches GLM-5.2 on some advanced reasoning tasks using roughly one-third to one-quarter of the inference compute, but the claims have not been independently verified; weights, a technical report and tools are slated for release later in October.

    IT之家 AI · 🔥 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