PCIe 4.0 x8/x8 vs x16/x16 for Dual GPUs: Does Bandwidth Choke Local LLM Tensor Parallelism?
📌 The Executive Verdict: Does PCIe 4.0 x8/x8 Choke Dual-GPU Inference? In dual-GPU workstations running local LLMs under tensor parallelism (such as…
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Gemma 4 on Amazon SageMaker: QAT Weights Decode 2.05x Faster Than bf16 on One L4
This article gives a short background on Amazon SageMaker real-time endpoints, then measures Gemma 4 E2B's quantization-aware trained (QAT) checkpoi…
Gemma 4 on Amazon SageMaker: QAT Weights Decode 2.05x Faster Than bf16 on One L4
This article gives a short background on Amazon SageMaker real-time endpoints, then measures Gemma 4 E2B's quantization-aware trained (QAT) checkpoi…
Gemma 4 on a Tesla T4, Part 2: The Minimum GCE VM and a Script to Drive It
This article provides a step by step deployment guide for **Gemma 4 E2B * to a Tesla T4 hosted GPU enabled system. A suite of Python MCP tools is bu…
Gemma 4 on a Tesla T4: QAT Weights Decode 1.79x Faster Than bf16
This article provides a step by step deployment guide for **Gemma 4 E2B * to a Tesla T4 hosted GPU enabled system. A suite of Python MCP tools is bu…
FastMCP Is Now MCPServer: Migrating a Python MCP Server to the MCP SDK 2.x
This article provides a step by step migration guide for a Python MCP server from the MCP Python SDK 1.x ( FastMCP ) to 2.x ( MCPServer ), followed…
KV Cache on 16 GB GPUs: Making Long Context Actually Fit
A model can advertise a 128K context window and still fail at 40K tokens on a 16 GB GPU. The architecture ceiling never promised that weights, KV ca…
From adapter to deployment: merging LoRA weights and serving with vLLM or a Space
A fine-tuned adapter sitting in a folder is not a product. FineTune Studio offers three inference paths — local, a vLLM server, or a Hugging Face Sp…
Deploying Inference Using NVIDIA Dynamo and vLLM
NVIDIA Dynamo is an open-source, high-throughput, low-latency inference framework for deploying large-scale generative AI and reasoning models acros…
vLLM v0.28.0: the breaking change small GPU users must read
vLLM v0.28.0 shipped on 26 August 2026 with 584 commits from 270 contributors (76 new) , and almost every headline in the release notes on GitHub is…
Serving Gemma4 with Rust on vLLM 🦀
This tutorial walks through installing and setting up the Rust toolchain for vLLM on an AWS EC2 G5g instance — Graviton2 (aarch64) with an NVIDIA T4…
Installing Rust for vLLM on Graviton: a G5g walk-through 🦀
This tutorial walks through installing and setting up the Rust toolchain for vLLM on an AWS EC2 G5g instance — Graviton2 (aarch64) with an NVIDIA T4…
Running Gemma 4 on EC2 G5g: Graviton2 AMD with NVIDIA GPU
A field report on serving Google's Gemma 4 E2B on AWS EC2 **G5g * — a Graviton2 (aarch64) host with an NVIDIA T4G (Turing, SM 7.5) GPU. Three obstac…
Hermes c локальными LLM vs GigaCode: сравниваем агентов на реальных PHP/Symfony-задачах
На YouTube очень часто мне попадаются видео, где сравнивают разные LLM для вайбкодеров: сделай 3D-аквариум с рыбками, напиши игру, слепи лендинг. Но…
Self-hosting a lite agent backend on one TPU: Gemma 4 E2B + vLLM on a v5e-1
Self-hosting a lite agent backend on one TPU chip A single Google Cloud TPU v5e chip — 16 GB of HBM, about $0.58/hour on spot — will serve google/ge…
Kimi K3 Open Weights Are Here: How to Self-Host the 2.8T-Parameter Model (Hardware, vLLM, and Data Sovereignty)
Kimi K3 Open Weights Are Here: How to Self-Host the 2.8T-Parameter Model On July 27, 2026, Moonshot AI released the open weights for Kimi K3 -- a 2.…
What 90% Line-Rate Utilization on a Single 100GbE Port Means: Analyzing Network Bottlenecks in Inference Storage
In LLM inference clusters, the core bottleneck for KV Cache storage acceleration often lies not in the storage medium itself, but in network bandwid…
Gemma 4 E2B on a Single TPU v6e Chip: A Serving Deep Dive
Measured 2026-07-21 on vllm/vllm-tpu:nightly (vLLM 0.23.1rc1.dev1076), a GCE flex-start ct6e-standard-1t (one TPU v6e chip, 32 GB HBM) in europe-wes…
Does a Second GPU Increase Ollama's Context Window? (Quadro P2000 + RTX 3090 Tested)
TL;DR Short version: no. I dropped a much older GPU ( Quadro P2000, 5GB, Pascal, 2016 ) next to an RTX 3090 (24GB, Ampere) on the same box, ran the…
DSpark на двух DGX Spark: порт, баг на одну строку и бенчмарки, которые пришлось мерить заново
DeepSeek выпустил DSpark — спекулятивный декодер для V4. В окне 27–30 июня 2026 рабочего публичного пути для GB10 …
Облачная LLM на 16 ГБ VRAM — часть 3: ChatGPT-интерфейс для ваших LangGraph-агентов
Финал цикла про облачную LLM на 16 ГБ VRAM. За две предыдущие статьи мы подняли собственную локальную модель на облачном сервере с GPU на 16 ГБ VRAM…
DeepSeek‑V4‑Flash на двух DGX Spark: как мы убрали очередь и получили multi‑user
Подняли DeepSeek‑V4‑Flash на двух GB10, упёрлись в потолок consumer Blackwell, прошли три тупика со спекулятивным декодингом — и в итоге получили па…
Ubuntu 26.04 на клоне DGX Spark (Asus GX10)
DGX Spark и его клоны поставляются с DGX OS (фактически, Ubuntu 24.04 с кучей дополнительных пакетов от Nvidia). Причем, драйвера используются довол…
Qwen3.6-35B NVFP4 runs on one H100 — A100 owners are out
NVIDIA published nvidia/Qwen3.6-35B-A3B-NVFP4 on May 28, 2026 — a post-training FP4-quantized variant of Alibaba's 35B MoE model that fits on a sing…
I built an open-source alternative to Microsoft's KAITO that works on ANY Kubernetes cluster
Six months ago, my team needed to deploy DeepSeek-R1 for internal use. We have a Kubernetes cluster — like everyone does in 2026 — so I started look…
Prefix caching at scale: when it saves you 80% of prefill cost, and the eviction policies that quietly turn it into 5%
Prefix caching at scale: when it saves you 80% of prefill cost, and the eviction policies that quietly turn it into 5% Your chatbot deploys 70B Llam…
KV cache quantization: what FP8/INT8 K and V actually buy you, and where they break
KV cache quantization: what FP8/INT8 K and V actually buy you, and where they break You just deployed a 70B Llama fine-tune on 8x H100s, and your se…
Как я разогнал Qwen3.6-27B до 73 токен/с в llama.cpp: параметры, которые реально работают
Локальные LLM сейчас — это действительно мощный инструмент. Они уже вплотную приблизились к проприетарным моделям вроде Claude, особенно в задачах к…
Как мы построили корпоративную LLM-платформу: архитектура, грабли и выводы
Обычно внедрение AI в компаниях происходит по такому сценарию: собрали одного ассистента, показали руководству, получили аплодисменты. Потом второго…
Ollama vs llama.cpp vs vLLM: Which Should You Use in 2026?
From the Best GPU for LLM archive. The canonical version has interactive calculators, an up-to-date GPU comparison table, and live pricing. Three to…