Morocco • Attijariwafa Bank eyes French start-up Mistral AI - Africa Intelligence
<a href="https://news.google.com/rss/articles/CBMivwFBVV95cUxNZTdNM1BWNjlJVndSdUF5MlgtUkZjT1JvQktMTklsWFVtMkdiXzhsMDY5a0c3cGhNdmtsY21pQjNoWnpmT1JLaGF4cEEzSHNvekZDUGtseHY2LVRqa0gxMllDbmlNY21DeGF4WXRqNzZCN3l2clEzajJMbHZ4a0RsczBFYkVzOEJGcnhMcGJxMFJuSkd3UFEzTnZEM0VOTU1kcXZVQmxqOHlNOGxGVmtCZTliQTdTOEo5VFQzd0ZvRQ?oc=5" target="_blank">Morocco • Attijariwafa Bank eyes French start-up Mistral AI</a> <font color="#6f6f6f">Africa Intelligence</font>
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mistralFrom one model to seven — what it took to make TurboQuant model-portable
<p>A KV cache compression plugin that only works on one model is a demo, not a tool. turboquant-vllm v1.0.0 shipped four days ago with one validated architecture: Molmo2. v1.3.0 validates seven — Llama 3.1, Mistral 7B, Qwen2.5, Phi-3-mini, Phi-4, Gemma-2, and Gemma-3. The path between those two points was more interesting than the destination.</p> <h2> What Changed </h2> <p><strong>Fused paged kernels (v1.2.0).</strong> The original architecture decompressed KV cache from TQ4 to FP16 in HBM, then ran standard attention on the result. The new fused kernel reads compressed blocks directly from vLLM's page table, decompresses in SRAM, and computes attention in a single pass. HBM traffic: 1,160 → 136 bytes per token.<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight pyth

Complete Guide to llm-d CNCF Sandbox — Kubernetes-Native Distributed LLM Inference
<h1> Complete Guide to llm-d CNCF Sandbox — Kubernetes-Native Distributed LLM Inference Framework </h1> <p>At KubeCon Europe 2026 in Amsterdam, IBM Research, Red Hat, and Google Cloud jointly donated <strong>llm-d</strong> to the CNCF as a Sandbox project. Backed by founding partners including NVIDIA, CoreWeave, AMD, Cisco, Hugging Face, Intel, Lambda, and Mistral AI, llm-d is a distributed inference framework designed to run large language model (LLM) inference at production scale on Kubernetes.</p> <p>If you've served models with vLLM or managed inference endpoints with KServe, you've likely felt the gap: <strong>vLLM is powerful but hits scaling walls as a single Pod, while KServe provides high-level abstractions but lacks inference-aware routing</strong>. llm-d fills exactly this gap a
Mistral AI Landed Military Contracts While U.S. Rivals Face Public Backlash - trendingtopics.eu
<a href="https://news.google.com/rss/articles/CBMiZ0FVX3lxTE5YRFBGNWZvV3BPTWRhVk95cFpmdjE1MXgwUXNZQmFvdURETkhjS2lERU5nNXl3T05SLTVBWHRvbHpoVWNrYTZ0TmZSRWJPYldrbFd3QTVnSzFveE4yM3pLbVBwZ0NQblE?oc=5" target="_blank">Mistral AI Landed Military Contracts While U.S. Rivals Face Public Backlash</a> <font color="#6f6f6f">trendingtopics.eu</font>
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From one model to seven — what it took to make TurboQuant model-portable
<p>A KV cache compression plugin that only works on one model is a demo, not a tool. turboquant-vllm v1.0.0 shipped four days ago with one validated architecture: Molmo2. v1.3.0 validates seven — Llama 3.1, Mistral 7B, Qwen2.5, Phi-3-mini, Phi-4, Gemma-2, and Gemma-3. The path between those two points was more interesting than the destination.</p> <h2> What Changed </h2> <p><strong>Fused paged kernels (v1.2.0).</strong> The original architecture decompressed KV cache from TQ4 to FP16 in HBM, then ran standard attention on the result. The new fused kernel reads compressed blocks directly from vLLM's page table, decompresses in SRAM, and computes attention in a single pass. HBM traffic: 1,160 → 136 bytes per token.<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight pyth
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