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    <title>flozi00 TechHub</title>
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    <description>Deep dives into servers, GPUs, AI infrastructure and modern IT systems.</description>
    <language>en</language>
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      <title>LLM Inference Calculator &amp; Break-Even Analyzer</title>
      <link>https://flozi.net/en/tools/llm-inference-calculator</link>
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      <pubDate>Tue, 22 Sep 2026 00:00:00 GMT</pubDate>
      <description>Interactive planning tools for LLM serving: VRAM capacity, decode throughput, prefill latency, and the batch size where decode stops being bandwidth-limited — plus the maximum sequence length at that operating point.</description>
      <category>ai</category>
      <category>gpu</category>
      <category>inference</category>
      <category>vllm</category>
      <category>deep-learning</category>
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      <category>calculator</category>
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    <item>
      <title>LLM Inference Economics: Hardware Limits, Costs &amp; Cloud Break-Even</title>
      <link>https://flozi.net/en/tools/llm-inference-economics</link>
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      <pubDate>Tue, 22 Sep 2026 00:00:00 GMT</pubDate>
      <description>From physics to euros: memory and compute limits of LLM inference, which models fit which hardware, token yield per GPU, own hardware vs cloud break-even, and open-weight models vs proprietary API token costs.</description>
      <category>ai</category>
      <category>gpu</category>
      <category>inference</category>
      <category>economics</category>
      <category>cloud</category>
      <category>deep-learning</category>
      <category>tools</category>
      <category>calculator</category>
    </item>
    <item>
      <title>Why Parakeet (FastConformer-TDT) Shines: Practical Advantages Over Encoder–Decoder ASR</title>
      <link>https://flozi.net/en/guides/ai/asr/why-parakeet-fastconformer-tdt-beats-encoder-decoder</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/ai/asr/why-parakeet-fastconformer-tdt-beats-encoder-decoder</guid>
      <pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate>
      <description>A technical breakdown of why Parakeet’s FastConformer + Token-and-Duration Transducer design is a strong fit for production ASR: streaming latency, throughput, long-form audio, and fast domain adaptation via external N-gram LMs.</description>
      <category>ai</category>
      <category>guide</category>
      <category>asr</category>
      <category>speech</category>
      <category>datacenter</category>
      <category>machine-learning</category>
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    <item>
      <title>My Journey: Obsession, Failure, and AI as an Anchor</title>
      <link>https://flozi.net/en/my-journey</link>
      <guid isPermaLink="true">https://flozi.net/en/my-journey</guid>
      <pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate>
      <description>A personal story about autism, entrepreneurship, mental health, and how AI became my anchor.</description>
      <category>about</category>
      <category>career</category>
      <category>ai</category>
      <category>autism</category>
      <category>personal</category>
    </item>
    <item>
      <title>The Router&apos;s Dilemma: A Hardware-Centric Deep Dive into Expert Parallelism</title>
      <link>https://flozi.net/en/guides/ai/scaling/expert_parallel</link>
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      <pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate>
      <description>A deep dive into the hardware mechanics of Expert Parallelism (EP), explaining how Mixture-of-Experts (MoE) models route tokens across GPUs and the challenges of All-to-All communication.</description>
      <category>ai</category>
      <category>gpu</category>
      <category>hardware</category>
      <category>guide</category>
      <category>scaling</category>
      <category>deep-learning</category>
      <category>moe</category>
    </item>
    <item>
      <title>Slicing the Silicon: A Hardware-Centric Deep Dive into Tensor Parallelism</title>
      <link>https://flozi.net/en/guides/ai/scaling/tensor_parallel</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/ai/scaling/tensor_parallel</guid>
      <pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate>
      <description>A deep dive into the hardware mechanics of Tensor Parallelism, explaining how matrix multiplication is split across GPUs and why communication bandwidth is the critical bottleneck.</description>
      <category>ai</category>
      <category>gpu</category>
      <category>hardware</category>
      <category>guide</category>
      <category>scaling</category>
      <category>deep-learning</category>
    </item>
    <item>
      <title>Pumping Data Upstream: A Hardware-Centric Deep Dive into Pipeline Parallelism</title>
      <link>https://flozi.net/en/guides/ai/scaling/pipeline_parallel</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/ai/scaling/pipeline_parallel</guid>
      <pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate>
      <description>A deep dive into the hardware mechanics of Pipeline Parallelism, explaining how layers are partitioned across GPUs and how scheduling algorithms like 1F1B manage the pipeline bubble.</description>
      <category>ai</category>
      <category>gpu</category>
      <category>hardware</category>
      <category>guide</category>
      <category>scaling</category>
      <category>deep-learning</category>
    </item>
    <item>
      <title>Selecting the Right GPU for Qwen3 Inference</title>
      <link>https://flozi.net/en/guides/ai/gpu-model-efficiency-playbook</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/ai/gpu-model-efficiency-playbook</guid>
      <pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate>
      <description>A practical overview that matches NVIDIA RTX PRO 6000, H200, and DGX Station platforms to Qwen3 model sizes using the ops:byte method from the LLM inference math guide.</description>
      <category>ai</category>
      <category>datacenter</category>
      <category>gpu</category>
      <category>deep-learning</category>
      <category>hardware</category>
      <category>guide</category>
    </item>
    <item>
      <title>Understanding LLM VRAM Requirements: A Mathematical Deep Dive</title>
      <link>https://flozi.net/en/guides/ai/llm-vram-calculator-explained</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/ai/llm-vram-calculator-explained</guid>
      <pubDate>Fri, 31 Oct 2025 00:00:00 GMT</pubDate>
      <description>Comprehensive guide to calculating GPU memory requirements for Large Language Model inference and training with practical examples using Qwen3-VL-32B.</description>
      <category>ai</category>
      <category>machine-learning</category>
      <category>gpu</category>
      <category>datacenter</category>
      <category>deep-learning</category>
    </item>
    <item>
      <title>A Practical Guide to LLM Inference Math: From Theory to Hardware</title>
      <link>https://flozi.net/en/guides/ai/llm-inference-math</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/ai/llm-inference-math</guid>
      <pubDate>Fri, 31 Oct 2025 00:00:00 GMT</pubDate>
      <description>Essential mathematical formulas and calculations for profiling LLM inference performance, determining compute vs memory bottlenecks, and selecting optimal GPU hardware.</description>
      <category>ai</category>
      <category>machine-learning</category>
      <category>gpu</category>
      <category>datacenter</category>
      <category>deep-learning</category>
      <category>hardware</category>
    </item>
    <item>
      <title>About Florian Zimmermeister</title>
      <link>https://flozi.net/en/about</link>
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      <pubDate>Thu, 30 Oct 2025 00:00:00 GMT</pubDate>
      <description>Learn about flozi00 - passionate about speech recognition, accessibility, and AI, with expertise in server infrastructure and systems.</description>
      <category>about</category>
      <category>cv</category>
      <category>ai</category>
      <category>datacenter</category>
    </item>
    <item>
      <title>Solving NVIDIA Driver Issues on Ubuntu 24.04: The `nokaslr` Solution Explained</title>
      <link>https://flozi.net/en/guides/linux/solving-nvidia-driver-issues-on-ubuntu-24-04-with-nokaslr</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/linux/solving-nvidia-driver-issues-on-ubuntu-24-04-with-nokaslr</guid>
      <pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate>
      <description>A guide to fixing NVIDIA driver problems on Ubuntu 24.04 by using the &apos;nokaslr&apos; kernel parameter to resolve conflicts with Kernel Address Space Layout Randomization (KASLR).</description>
      <category>linux</category>
      <category>systems</category>
      <category>nvidia</category>
      <category>drivers</category>
      <category>ubuntu</category>
      <category>guide</category>
    </item>
    <item>
      <title>Install NVIDIA Drivers, Docker, and GPU Support on Linux</title>
      <link>https://flozi.net/en/guides/linux/installing-nvidia-drivers-docker-gpu-support</link>
      <guid isPermaLink="true">https://flozi.net/en/guides/linux/installing-nvidia-drivers-docker-gpu-support</guid>
      <pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate>
      <description>A beginner-friendly guide to installing NVIDIA drivers, Docker, and the NVIDIA Container Toolkit for GPU-accelerated containers on Ubuntu.</description>
      <category>linux</category>
      <category>ubuntu</category>
      <category>nvidia</category>
      <category>docker</category>
      <category>gpu</category>
      <category>guide</category>
    </item>
    <item>
      <title>Qwen3-Next: A Deep Dive into Alibaba&apos;s Hybrid MoE Powerhouse</title>
      <link>https://flozi.net/en/ai/models/qwen/qwen3-next</link>
      <guid isPermaLink="true">https://flozi.net/en/ai/models/qwen/qwen3-next</guid>
      <pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate>
      <description>A technical breakdown of the Qwen3-Next-80B-A3B-Instruct model, exploring its Hybrid MoE architecture, FP8 precision, and how it achieves efficiency with 80B total parameters but only 3B active.</description>
      <category>ai</category>
      <category>machine-learning</category>
      <category>llm</category>
      <category>qwen</category>
      <category>moe</category>
      <category>fp8</category>
      <category>nvidia</category>
      <category>alibaba</category>
    </item>
    <item>
      <title>NVIDIA B200 vs GB200: Comprehensive Efficiency Benchmark Comparison</title>
      <link>https://flozi.net/en/hardware/nvidia/benchmarks/b200-vs-gb200-efficiency-comparison</link>
      <guid isPermaLink="true">https://flozi.net/en/hardware/nvidia/benchmarks/b200-vs-gb200-efficiency-comparison</guid>
      <pubDate>Tue, 28 Oct 2025 00:00:00 GMT</pubDate>
      <description>Detailed performance analysis comparing NVIDIA Blackwell B200 and GB200 accelerators across various system configurations, revealing efficiency differences of up to 42%</description>
      <category>nvidia</category>
      <category>blackwell</category>
      <category>b200</category>
      <category>gb200</category>
      <category>benchmark</category>
      <category>performance</category>
      <category>efficiency</category>
      <category>gpu</category>
      <category>accelerator</category>
    </item>
    <item>
      <title>NVIDIA NVLink Solutions - High-Speed GPU Interconnect</title>
      <link>https://flozi.net/en/hardware/nvidia/communication/nvidia-nvlink</link>
      <guid isPermaLink="true">https://flozi.net/en/hardware/nvidia/communication/nvidia-nvlink</guid>
      <pubDate>Tue, 28 Oct 2025 00:00:00 GMT</pubDate>
      <description>Comprehensive guide to NVIDIA NVLink technology for multi-GPU systems, including configuration, testing, and optimization for AI and HPC workloads</description>
      <category>nvidia</category>
      <category>nvlink</category>
      <category>gpu</category>
      <category>interconnect</category>
      <category>hpc</category>
      <category>hardware</category>
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