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        <description>Inference Engineering, In Depth</description>
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            <title><![CDATA[DeepGEMM: The Matrix Multiplications Behind DeepSeek's Models]]></title>
            <link>https://theservingstack.com/deepgemm</link>
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            <pubDate>Sun, 25 Oct 2026 07:30:00 GMT</pubDate>
            <description><![CDATA[What DeepGEMM computes for DeepSeek's models: block-scaled FP8 and FP4 matrix multiplications, grouped GEMMs for MoE experts, and the fused Mega MoE kernel.]]></description>
            <author>The Serving Stack</author>
            <category>gpu-for-inference</category>
            <category>deepgemm</category>
            <category>gpu</category>
            <category>quantization</category>
            <category>moe</category>
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            <title><![CDATA[Expert Parallelism, and What DeepEP Does for It]]></title>
            <link>https://theservingstack.com/expert-parallelism-deepep</link>
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            <pubDate>Sat, 24 Oct 2026 07:30:00 GMT</pubDate>
            <description><![CDATA[Why serving a big MoE model means spreading its experts over many GPUs, what dispatch and combine cost, and how DeepEP and its V2 redesign make it fast.]]></description>
            <author>The Serving Stack</author>
            <category>serving-in-production</category>
            <category>parallelism</category>
            <category>deepep</category>
            <category>moe</category>
            <category>sglang</category>
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        <item>
            <title><![CDATA[DeepInfer: What DeepSeek Open-Sourced, and What It Kept]]></title>
            <link>https://theservingstack.com/deepinfer</link>
            <guid isPermaLink="false">https://theservingstack.com/deepinfer</guid>
            <pubDate>Fri, 23 Oct 2026 07:30:00 GMT</pubDate>
            <description><![CDATA[DeepSeek's open-source engine runs one model on Blackwell and Ascend: why it came out, what the code shows, what was kept back, and what it means for vLLM users.]]></description>
            <author>The Serving Stack</author>
            <category>inference-engines</category>
            <category>deepseek</category>
            <category>deepinfer</category>
            <category>sglang</category>
            <category>ascend</category>
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        <item>
            <title><![CDATA[Qwen3.8-Flash-Next, Layer by Layer]]></title>
            <link>https://theservingstack.com/qwen38-flash-next</link>
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            <pubDate>Thu, 22 Oct 2026 07:30:00 GMT</pubDate>
            <description><![CDATA[Qwen's preview of its next architecture, piece by piece: linear and sparse attention, a gated four-branch residual, 512 experts and an n-gram memory, and what each costs to serve.]]></description>
            <author>The Serving Stack</author>
            <category>llms-explained</category>
            <category>qwen</category>
            <category>moe</category>
            <category>hybrid-attention</category>
            <category>vllm</category>
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        <item>
            <title><![CDATA[Engram: Memory You Look Up Instead of Compute]]></title>
            <link>https://theservingstack.com/engram-conditional-memory</link>
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            <pubDate>Wed, 21 Oct 2026 07:30:00 GMT</pubDate>
            <description><![CDATA[DeepSeek's Engram adds a hashed n-gram memory that a model looks up instead of computing: how it works, why it helps, and how DeepSeek-V4.1 and Qwen serve it.]]></description>
            <author>The Serving Stack</author>
            <category>llms-explained</category>
            <category>engram</category>
            <category>moe</category>
            <category>deepseek</category>
            <category>qwen</category>
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        <item>
            <title><![CDATA[Benchmark an Inference Server Without Lying to Yourself]]></title>
            <link>https://theservingstack.com/benchmarking-inference-servers</link>
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            <pubDate>Sun, 18 Oct 2026 04:50:30 GMT</pubDate>
            <description><![CDATA[Common load-test mistakes made on purpose next to the correct run, on one vLLM server, so each rule comes with how far it moves the numbers.]]></description>
            <author>The Serving Stack</author>
            <category>serving-in-production</category>
            <category>benchmarking</category>
            <category>vllm</category>
            <category>latency</category>
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        <item>
            <title><![CDATA[Quantization-Aware Training: When the Model Learns Its Own Precision]]></title>
            <link>https://theservingstack.com/quantization-aware-training</link>
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            <pubDate>Sun, 18 Oct 2026 04:48:09 GMT</pubDate>
            <description><![CDATA[Why Kimi K2 Thinking ships in INT4 and gpt-oss and K3 in MXFP4: training with rounding in the loop, explained and measured on a small model.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>qat</category>
            <category>mxfp4</category>
            <category>int4</category>
            <category>training</category>
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        <item>
            <title><![CDATA[Your First CUDA Kernel, Compiled and Profiled]]></title>
            <link>https://theservingstack.com/first-cuda-kernel</link>
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            <pubDate>Sat, 17 Oct 2026 22:02:32 GMT</pubDate>
            <description><![CDATA[Write SAXPY in CUDA, compile it for a Blackwell GPU, check it, time it, and read what Nsight Systems and Nsight Compute say about it.]]></description>
            <author>The Serving Stack</author>
            <category>cuda-for-inference</category>
            <category>cuda</category>
            <category>gpu</category>
            <category>profiling</category>
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        <item>
            <title><![CDATA[A 27B Model as Your Local Agent: What It Can and Can't Do]]></title>
            <link>https://theservingstack.com/local-27b-agent</link>
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            <pubDate>Sat, 17 Oct 2026 22:01:08 GMT</pubDate>
            <description><![CDATA[Real coding-agent turns replayed on a 27B model on one desk-side GPU: what the prefix cache saves, how many agents fit, how well it calls tools.]]></description>
            <author>The Serving Stack</author>
            <category>building-with-ai</category>
            <category>agents</category>
            <category>tool-use</category>
            <category>sglang</category>
            <category>prefix-caching</category>
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        <item>
            <title><![CDATA[Rotations for 4-Bit Activations: QuaRot, SpinQuant, and Friends]]></title>
            <link>https://theservingstack.com/rotation-quantization</link>
            <guid isPermaLink="false">https://theservingstack.com/rotation-quantization</guid>
            <pubDate>Sat, 17 Oct 2026 21:56:30 GMT</pubDate>
            <description><![CDATA[A rotation flattens activation outliers and, on real layers, rescues INT4 activations. On Qwen3-8B in NVFP4, it cost 5 GSM8K points instead.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>rotations</category>
            <category>nvfp4</category>
            <category>llm-compressor</category>
            <category>vllm</category>
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        <item>
            <title><![CDATA[A GPU in One Picture: SMs, Warps, and Memory]]></title>
            <link>https://theservingstack.com/gpu-in-one-picture</link>
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            <pubDate>Sat, 17 Oct 2026 21:50:17 GMT</pubDate>
            <description><![CDATA[The parts of a GPU that decide inference speed, from streaming multiprocessors to DRAM, with one real GPU's numbers and three measurements.]]></description>
            <author>The Serving Stack</author>
            <category>gpu-for-inference</category>
            <category>gpu</category>
            <category>cuda</category>
            <category>inference</category>
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        <item>
            <title><![CDATA[Measuring the Damage: Evaluations That Catch Quantization Errors]]></title>
            <link>https://theservingstack.com/quantization-evals</link>
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            <pubDate>Sat, 17 Oct 2026 21:47:32 GMT</pubDate>
            <description><![CDATA[GSM8K said every 4-bit format matched BF16. A noise baseline, paired comparisons, token-level KL, and a code benchmark showed which ones did not.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>evals</category>
            <category>vllm</category>
            <category>benchmarking</category>
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        <item>
            <title><![CDATA[SmoothQuant and W8A8: Quantizing Activations Too]]></title>
            <link>https://theservingstack.com/smoothquant-w8a8</link>
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            <pubDate>Sat, 17 Oct 2026 21:46:02 GMT</pubDate>
            <description><![CDATA[Why activation outliers break INT8, how SmoothQuant moves them into the weights, and what it changes for FP8 W8A8, measured on Qwen3-8B with vLLM.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>smoothquant</category>
            <category>fp8</category>
            <category>llm-compressor</category>
            <category>vllm</category>
            <enclosure url="https://media.theservingstack.com/images/img_mvcyniqxn622uc/smoothquant-w8a8-cover.png" length="0" type="image/png"/>
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        <item>
            <title><![CDATA[Quantizing the KV Cache: FP8 and Below]]></title>
            <link>https://theservingstack.com/kv-cache-quantization</link>
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            <pubDate>Sat, 17 Oct 2026 21:43:13 GMT</pubDate>
            <description><![CDATA[Seven KV cache formats in vLLM, from FP8 to 3-bit, on one model: how many more tokens fit, what long-context recall loses, and why speed follows the kernel.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>kv-cache</category>
            <category>vllm</category>
            <category>fp8</category>
            <category>long-context</category>
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        <item>
            <title><![CDATA[TTFT, ITL, Goodput: Measuring What Users Feel]]></title>
            <link>https://theservingstack.com/ttft-itl-goodput</link>
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            <pubDate>Sat, 17 Oct 2026 09:42:58 GMT</pubDate>
            <description><![CDATA[What TTFT, TPOT, ITL, end-to-end latency, and goodput each measure, how the tools compute them, and why peak throughput is the wrong target.]]></description>
            <author>The Serving Stack</author>
            <category>serving-in-production</category>
            <category>benchmarking</category>
            <category>observability</category>
            <category>vllm</category>
            <category>latency</category>
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        <item>
            <title><![CDATA[Prefill vs. Decode: Why the Second Token Is a Different Workload]]></title>
            <link>https://theservingstack.com/prefill-vs-decode</link>
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            <pubDate>Fri, 16 Oct 2026 23:53:04 GMT</pubDate>
            <description><![CDATA[Why a prompt token costs hundreds of times less than an answer token on the same weights: prefill and decode, seen in a kernel trace.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>prefill-decode</category>
            <category>gpu</category>
            <category>vllm</category>
            <category>inference</category>
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        <item>
            <title><![CDATA[From Prompt to Token: The Life of an LLM Request]]></title>
            <link>https://theservingstack.com/life-of-a-request</link>
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            <pubDate>Fri, 16 Oct 2026 23:43:01 GMT</pubDate>
            <description><![CDATA[One chat request followed through a real inference server, from chat template and tokens to prefill, decode, sampling, and streaming, each stage timed.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>inference</category>
            <category>serving</category>
            <category>vllm</category>
            <category>prefill-decode</category>
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        <item>
            <title><![CDATA[AWQ: Protecting the Weights That Matter]]></title>
            <link>https://theservingstack.com/awq-explained</link>
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            <pubDate>Sun, 11 Oct 2026 09:15:49 GMT</pubDate>
            <description><![CDATA[Which 1% of weights matter, why scaling them protects them, and the search behind AWQ, in a few lines of PyTorch on a real layer, then AWQ on Qwen3-8B with llm-compressor and vLLM against plain rounding, GPTQ and a published checkpoint.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>awq</category>
            <category>llm-compressor</category>
            <category>vllm</category>
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        <item>
            <title><![CDATA[GPTQ, Explained and Run]]></title>
            <link>https://theservingstack.com/gptq-explained</link>
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            <pubDate>Sat, 10 Oct 2026 13:01:28 GMT</pubDate>
            <description><![CDATA[How GPTQ trades weight error for output error, in 30 lines of PyTorch on a real layer, then how to run it with llm-compressor and serve it with vLLM, and which settings actually change the result on Qwen3-8B.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>gptq</category>
            <category>llm-compressor</category>
            <category>vllm</category>
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        <item>
            <title><![CDATA[GGUF Quant Types Decoded: K-Quants and I-Quants]]></title>
            <link>https://theservingstack.com/gguf-quant-types</link>
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            <pubDate>Sat, 10 Oct 2026 12:42:14 GMT</pubDate>
            <description><![CDATA[What Q4_K_M, IQ3_XXS, and the rest mean: the block layouts from llama.cpp's source, the real bits per weight of each file, and the weight error of each type on Qwen3-8B.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>gguf</category>
            <category>llama-cpp</category>
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        <item>
            <title><![CDATA[Post-Training Quantization: Rounding, Calibration, and the Outlier Problem]]></title>
            <link>https://theservingstack.com/ptq-and-outliers</link>
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            <pubDate>Sat, 10 Oct 2026 12:42:14 GMT</pubDate>
            <description><![CDATA[Why rounding each weight to the nearest value is not enough, what calibration data is for, and the two kinds of outliers in a real model's activations, measured on Qwen3-8B.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>ptq</category>
            <category>outliers</category>
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        <item>
            <title><![CDATA[How Much Does Each Bit Cost? Size, Speed, and Quality, Measured]]></title>
            <link>https://theservingstack.com/bits-size-quality</link>
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            <pubDate>Sat, 10 Oct 2026 12:42:13 GMT</pubDate>
            <description><![CDATA[Qwen3-8B in thirteen formats from BF16 to 3-bit GGUF, on one GPU with vLLM: memory, decode speed, prompt reading, perplexity, and GSM8K, plus the formats that would not run.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>benchmark</category>
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        <item>
            <title><![CDATA[Where Low Precision Bites: Rounding, Overflow, and Accumulation]]></title>
            <link>https://theservingstack.com/low-precision-pitfalls</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:15 GMT</pubDate>
            <description><![CDATA[Five places where low precision breaks real computations, each reproduced on Qwen3-8B's own tensors, and the fix every serious implementation applies.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>numerics</category>
            <category>debugging</category>
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        <item>
            <title><![CDATA[Quantization, Mapped: Weights, Activations, KV Cache]]></title>
            <link>https://theservingstack.com/quantization-map</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:15 GMT</pubDate>
            <description><![CDATA[What can be quantized in a model, what each saves (memory, bandwidth, compute), and what each risks, with Qwen3-8B's numbers.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>overview</category>
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        <item>
            <title><![CDATA[Which GPU Computes Which Format: Ampere to Rubin, MI300 to MI355]]></title>
            <link>https://theservingstack.com/gpu-format-support</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:14 GMT</pubDate>
            <description><![CDATA[Which number formats each data-center GPU computes natively, at what rate, from the vendors' own pages: A100 to Rubin, MI300X to MI355X.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>gpu</category>
            <category>hardware</category>
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        <item>
            <title><![CDATA[NVFP4: Two Levels of Scale]]></title>
            <link>https://theservingstack.com/nvfp4-explained</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:14 GMT</pubDate>
            <description><![CDATA[How NVFP4 differs from MXFP4: 16-value blocks, FP8 block scales, and a per-tensor scale, with what each part buys measured on Qwen3-8B.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>nvfp4</category>
            <category>fp4</category>
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            <title><![CDATA[Microscaling: MXFP8, MXFP6, MXFP4 and Block Scales]]></title>
            <link>https://theservingstack.com/microscaling-mx-formats</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:14 GMT</pubDate>
            <description><![CDATA[The OCP microscaling formats: 32 values sharing one power-of-two scale, what MXFP8, MXFP6, and MXFP4 cost in accuracy on a real model, and why the scale rule matters.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>mxfp4</category>
            <category>microscaling</category>
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            <title><![CDATA[Integers for AI: INT8, INT4, Scales, and Zero Points]]></title>
            <link>https://theservingstack.com/integers-for-ai</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:13 GMT</pubDate>
            <description><![CDATA[How integers represent real weights: scales, zero points, per-tensor, per-channel, and per-group, measured on every weight of Qwen3-8B.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>int8</category>
            <category>int4</category>
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        <item>
            <title><![CDATA[FP8: E4M3, E5M2, and Why There Are Two]]></title>
            <link>https://theservingstack.com/fp8-formats</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:13 GMT</pubDate>
            <description><![CDATA[The two FP8 formats, what each is for, and how FP8 is scaled, measured on Qwen3-8B's weights and activations against INT8.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>fp8</category>
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        <item>
            <title><![CDATA[TF32 and Accumulation: What Tensor Cores Really Compute In]]></title>
            <link>https://theservingstack.com/tf32-and-accumulation</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:12 GMT</pubDate>
            <description><![CDATA[Inputs, products, and accumulators: the precision a matrix multiplication really uses, and why FP32 accumulation still matters.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>tf32</category>
            <category>tensor-cores</category>
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        <item>
            <title><![CDATA[FP16 vs. BF16: Same Size, Different Trade]]></title>
            <link>https://theservingstack.com/fp16-vs-bf16</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:12 GMT</pubDate>
            <description><![CDATA[Why training moved from FP16 to BF16, and why a BF16 model may still run fine in FP16: the same 16 bits split differently, measured on Qwen3-8B.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>fp16</category>
            <category>bf16</category>
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        <item>
            <title><![CDATA[How a Computer Stores a Real Number: Sign, Exponent, Mantissa]]></title>
            <link>https://theservingstack.com/floats-explained</link>
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            <pubDate>Fri, 09 Oct 2026 19:19:11 GMT</pubDate>
            <description><![CDATA[What the bits of a floating-point number mean, why range and precision trade against each other, with an interactive bit explorer.]]></description>
            <author>The Serving Stack</author>
            <category>formats</category>
            <category>fp32</category>
            <category>floating-point</category>
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            <title><![CDATA[Distillation: How Small Models Learn from Big Ones]]></title>
            <link>https://theservingstack.com/distillation</link>
            <guid isPermaLink="false">https://theservingstack.com/distillation</guid>
            <pubDate>Fri, 09 Oct 2026 19:19:11 GMT</pubDate>
            <description><![CDATA[What distillation is, the ways labs distill models, and how they use it on their own models and on others', from primary sources.]]></description>
            <author>The Serving Stack</author>
            <category>llms-explained</category>
            <category>distillation</category>
            <category>training</category>
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        <item>
            <title><![CDATA[Z.ai's GLM Models: From ChatGLM to GLM-5]]></title>
            <link>https://theservingstack.com/glm-models</link>
            <guid isPermaLink="false">https://theservingstack.com/glm-models</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[Z.ai's (Zhipu's) GLM models in order, from the GLM pretraining objective to agentic MoE models, the idea behind each step, and what each changed for the ecosystem.]]></description>
            <author>The Serving Stack</author>
            <category>open-model-labs</category>
            <category>glm</category>
            <category>zai</category>
            <category>zhipu</category>
            <category>moe</category>
            <category>agents</category>
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            <title><![CDATA[NVFP4 on Blackwell: Serving a 4-Bit Model End to End]]></title>
            <link>https://theservingstack.com/nvfp4-on-blackwell</link>
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            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[What NVFP4 stores, how vLLM serves it on Blackwell, and what it changes in memory, speed, and quality.]]></description>
            <author>The Serving Stack</author>
            <category>quantization</category>
            <category>nvfp4</category>
            <category>fp8</category>
            <category>blackwell</category>
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            <title><![CDATA[Speculative Decoding from First Principles]]></title>
            <link>https://theservingstack.com/speculative-decoding</link>
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            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[How a cheap guess and one verification pass produce several tokens per step, when it speeds things up and when it does not, measured.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>speculative-decoding</category>
            <category>vllm</category>
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            <title><![CDATA[Prefix Caching: When It Pays and How to Design Prompts for It]]></title>
            <link>https://theservingstack.com/prefix-caching</link>
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            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[Why a repeated prompt can cost almost nothing, how vLLM and SGLang find shared prefixes, what breaks a hit, and how to structure prompts for agents and chat.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>prefix-caching</category>
            <category>kv-cache</category>
            <category>vllm</category>
            <category>sglang</category>
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            <title><![CDATA[Chunked Prefill: Trading First-Token Time for Smooth Streaming]]></title>
            <link>https://theservingstack.com/chunked-prefill</link>
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            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[How engines split long prompts across steps, what the per-step token budget trades between time to first token and time per token, and how to set it.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>chunked-prefill</category>
            <category>scheduling</category>
            <category>vllm</category>
            <category>sglang</category>
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        <item>
            <title><![CDATA[Structured Output: How Engines Force Valid JSON, Token by Token]]></title>
            <link>https://theservingstack.com/structured-output</link>
            <guid isPermaLink="false">https://theservingstack.com/structured-output</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[How vLLM and SGLang guarantee valid JSON or a grammar: compiling a schema into a token mask, applying it at every step, and what it costs in latency and quality.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>structured-output</category>
            <category>json</category>
            <category>tool-calling</category>
            <category>vllm</category>
            <category>sglang</category>
            <category>xgrammar</category>
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        <item>
            <title><![CDATA[The Roofline for LLMs: Why Decode Is Memory-Bound]]></title>
            <link>https://theservingstack.com/roofline-for-llms</link>
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            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[Where prefill and decode sit against a GPU's compute and memory limits, and why batching, quantization, and speculative decoding work, with an interactive roofline.]]></description>
            <author>The Serving Stack</author>
            <category>gpu</category>
            <category>roofline</category>
            <category>decode</category>
            <category>memory-bandwidth</category>
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        <item>
            <title><![CDATA[MiniMax: Lightning Attention, Agents, and What They Tried]]></title>
            <link>https://theservingstack.com/minimax-models</link>
            <guid isPermaLink="false">https://theservingstack.com/minimax-models</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[MiniMax's models in order: lightning (linear) attention at a 4-million-token context, reasoning, the return to full attention for agents, and what each choice taught the ecosystem.]]></description>
            <author>The Serving Stack</author>
            <category>open-model-labs</category>
            <category>minimax</category>
            <category>linear-attention</category>
            <category>moe</category>
            <category>agents</category>
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        <item>
            <title><![CDATA[DeepSeek: From V1 to V4, the Models and the Breakthroughs]]></title>
            <link>https://theservingstack.com/deepseek-models</link>
            <guid isPermaLink="false">https://theservingstack.com/deepseek-models</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:25 GMT</pubDate>
            <description><![CDATA[DeepSeek's models in order, the idea behind each one (MoE, latent attention, FP8 training, RL reasoning, sparse attention), and what each changed for everyone else.]]></description>
            <author>The Serving Stack</author>
            <category>open-model-labs</category>
            <category>deepseek</category>
            <category>moe</category>
            <category>mla</category>
            <category>reasoning</category>
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        <item>
            <title><![CDATA[NVIDIA's Speech Stack: Parakeet, Canary, Nemotron Speech, and Magpie]]></title>
            <link>https://theservingstack.com/nvidia-speech-models</link>
            <guid isPermaLink="false">https://theservingstack.com/nvidia-speech-models</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:24 GMT</pubDate>
            <description><![CDATA[What NVIDIA's open speech models are for: Parakeet and Canary for recognition, Nemotron Speech for streaming, Magpie for synthesis, and how they fit a voice agent.]]></description>
            <author>The Serving Stack</author>
            <category>speech-ai</category>
            <category>nvidia</category>
            <category>nemotron</category>
            <category>parakeet</category>
            <category>canary</category>
            <category>magpie</category>
            <category>voice-agents</category>
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        <item>
            <title><![CDATA[Disaggregated Serving: Dynamo, llm-d, and When Splitting Prefill from Decode Pays]]></title>
            <link>https://theservingstack.com/disaggregated-serving</link>
            <guid isPermaLink="false">https://theservingstack.com/disaggregated-serving</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:24 GMT</pubDate>
            <description><![CDATA[Why production stacks run prefill and decode on separate GPUs, how NVIDIA Dynamo and llm-d move the KV cache between them, and when the split pays.]]></description>
            <author>The Serving Stack</author>
            <category>serving</category>
            <category>disaggregation</category>
            <category>dynamo</category>
            <category>llm-d</category>
            <category>vllm</category>
            <category>sglang</category>
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        <item>
            <title><![CDATA[Kimi: Moonshot AI's Models, from Long Context to Trillion-Parameter Agents]]></title>
            <link>https://theservingstack.com/kimi-models</link>
            <guid isPermaLink="false">https://theservingstack.com/kimi-models</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:24 GMT</pubDate>
            <description><![CDATA[Moonshot AI's Kimi models in order, from long-context chat to trillion-parameter agentic MoE models, the idea behind each step, and what each changed for the ecosystem.]]></description>
            <author>The Serving Stack</author>
            <category>open-model-labs</category>
            <category>kimi</category>
            <category>moonshot</category>
            <category>moe</category>
            <category>agents</category>
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        <item>
            <title><![CDATA[Speech Recognition with Whisper on vLLM: Mel Spectrograms, Streaming, and Who Spoke When]]></title>
            <link>https://theservingstack.com/asr-whisper-vllm</link>
            <guid isPermaLink="false">https://theservingstack.com/asr-whisper-vllm</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:24 GMT</pubDate>
            <description><![CDATA[How Whisper turns audio into text, from mel spectrograms to an encoder-decoder; serving it with vLLM, transcribing in real time, and labeling speakers.]]></description>
            <author>The Serving Stack</author>
            <category>speech-ai</category>
            <category>asr</category>
            <category>whisper</category>
            <category>vllm</category>
            <category>diarization</category>
            <category>realtime</category>
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        <item>
            <title><![CDATA[Text-to-Speech in 2026: How the Models Work and Who Leads]]></title>
            <link>https://theservingstack.com/tts-landscape</link>
            <guid isPermaLink="false">https://theservingstack.com/tts-landscape</guid>
            <pubDate>Tue, 06 Oct 2026 08:26:24 GMT</pubDate>
            <description><![CDATA[How modern text-to-speech models turn text into audio tokens and waveforms, the open models that lead today, and how to choose one for latency, quality, and voices.]]></description>
            <author>The Serving Stack</author>
            <category>speech-ai</category>
            <category>tts</category>
            <category>voice</category>
            <category>audio-codecs</category>
            <enclosure url="https://media.theservingstack.com/images/img_mv8ml6j23m825o/tts-landscape-cover.png" length="0" type="image/png"/>
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        <item>
            <title><![CDATA[Choosing an Inference Server: The Landscape, from Data Centers to Laptops]]></title>
            <link>https://theservingstack.com/choosing-an-inference-engine</link>
            <guid isPermaLink="false">https://theservingstack.com/choosing-an-inference-engine</guid>
            <pubDate>Sun, 04 Oct 2026 21:16:16 GMT</pubDate>
            <description><![CDATA[Inference servers in three groups: production engines and their stacks, local apps built on llama.cpp, and enthusiasts' specialized engines, with how to pick.]]></description>
            <author>The Serving Stack</author>
            <category>inference-engines</category>
            <category>serving</category>
            <category>vllm</category>
            <category>sglang</category>
            <category>tensorrt-llm</category>
            <category>llama-cpp</category>
            <category>ollama</category>
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        <item>
            <title><![CDATA[Paged Attention: Virtual Memory for the KV Cache]]></title>
            <link>https://theservingstack.com/paged-attention</link>
            <guid isPermaLink="false">https://theservingstack.com/paged-attention</guid>
            <pubDate>Sun, 04 Oct 2026 21:16:16 GMT</pubDate>
            <description><![CDATA[How serving engines hand out KV cache memory in 16-token blocks instead of reserving the maximum, how the same blocks let requests share a prompt, and what that saves in memory and time to first token.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>kv-cache</category>
            <category>paged-attention</category>
            <category>vllm</category>
            <category>serving</category>
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        <item>
            <title><![CDATA[Continuous Batching, Animated: How a Server Keeps the GPU Busy]]></title>
            <link>https://theservingstack.com/continuous-batching</link>
            <guid isPermaLink="false">https://theservingstack.com/continuous-batching</guid>
            <pubDate>Sun, 04 Oct 2026 17:24:43 GMT</pubDate>
            <description><![CDATA[Why serving engines re-form the batch at every decode step, what it buys in throughput and latency, and the settings that control it.]]></description>
            <author>The Serving Stack</author>
            <category>request-to-tokens</category>
            <category>batching</category>
            <category>scheduling</category>
            <category>vllm</category>
            <category>serving</category>
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