Holo4 27B vs 35B-A3B: Which Fits Your Agent?
Holo4 27B vs 35B-A3B compares long-task reliability, task economics, and weight licenses so builders can choose an agent model.
WaveSpeed Blog5 min read
vLLM
244 stories
The State of Serene, September 2026
Issue #4 of The State of Serene: faster search on every core and from a cold cache, regex and fuzzy search that always answer, text analyzers as expressions, HNSW, the docs inside the binary, OpenTelemetry ingestion, faster time histograms, MATCH_RECOGNIZE, spatial and AI functions.
SereneDB Blog26 min read
KV Cache: What It Stores and How Much Memory It Needs
KV cache explained: what it stores, a per-model memory table from 8K to 128K context, and the Ollama, llama.cpp, LM Studio and vLLM settings that shrink it.
Zentask Blog10 min read
Llama.cpp alternatives for running local agents: ds4, Magnitude and Strata against llama.cpp, vLLM and Ollama
ds4 runs big MoE models on 96 GB Macs, Magnitude tunes kernels to your device, Strata puts one model on a gaming PC. Every speed claim is self-reported.
Stackness Blog10 min read
Kolibri: A Sovereign Open-Weight Model
Kolibri is an English-German Mixture-of-Experts model with 78B total and 3B active parameters, a context of up to 1M tokens and open weights under Apache 2.0, specialized for sovereign, mission-critical work.
Lobsters26 min read
Best Online GenAI Course for Developers and Working Professionals
Developers are skeptical by default, and that is a good thing when choosing a Gen-AI course. Here is a technical checklist to score any online GenAI course before you enrol. The scoring checklist Give each item 1 point. A strong course should score 10 or more out of 12.
Dev.to AI2 min read
How to Run Kolibri-1 Locally: VRAM and Hardware Requirements
Kolibri-1 needs roughly 78GB of FP8 weights and multi-GPU setups to serve. Here's the full vLLM, VRAM, and hardware guide.
MindStudio Blog7 min read
How to Run Kolibri-1 Locally: Aleph Alpha's German-English MoE Model
Kolibri-1 is Aleph Alpha's 78B MoE model with 3.46B active params. Here's what hardware you need and how to serve it with vLLM.
MindStudio Blog7 min read
Kolibri-1: Aleph Alpha's Open-Weight German-English MoE Model
Kolibri-1 is Aleph Alpha's 78B MoE model with 3.5B active params, 1M context, and Apache 2.0 license, built for German and English.
MindStudio Blog9 min read
not much happened today
AI News for 10/1/2026-10/2/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!
Latent Space25 min read
Do taxímetro de APIs ao custo quase Zero: Por que a Soberania de IA é decisão de engenharia
Parece cena de ficção científica: uma cancela de pedágio aos pés da pirâmide da Tyrell Corp cobrando uma taxa por cada bit do seu código. Daria pra chamar de "imposto de token" essa tentativa de criar um lock-in onde três ou quatro fornecedores de IA precificam cada linha de código que o seu time…
Dev.to AI12 min read
Prime Intellect Launches Prime Inference: Serverless and Reserved Serving for Frontier Open Models
Prime Intellect has launched Prime Inference, an OpenAI-compatible platform for serving frontier open models on NVIDIA Blackwell. Its GLM-5.3 deployment uses Dynamo, vLLM and NVFP4 KV compression to serve 66 sessions per prefill group at 101 tok/s per user.
MarkTechPost4 min read
IQuest-Q1: A 320B MoE Model Built for Agentic Coding
IQuest-Q1 is a 320B MoE model with 15B active params and 512K context, designed for agentic coding with Claude Code and Codex CLI.
MindStudio Blog8 min read
IQuest-Q1: How to Self-Host the 320B Agentic Coding Model
IQuest-Q1 is a 320B MoE coding model with 15B active params and 512K context. Here's what it takes to run it with SGLang or vLLM.
MindStudio Blog8 min read
Limit 1B Violetto: Testing Paradigma's Tiny Math Model Locally
A local test of Limit 1B Violetto, Paradigma's 1B math specialist, covering install steps, VRAM use, and its tendency to drift off-topic.
MindStudio Blog7 min read
Deploy OpenJev on Runpod Serverless (Step-by-Step Guide)
Learn how to deploy OpenJev, a 27B structured-decision model, on a Runpod Serverless load balancer endpoint, then send requests with cURL and Python.
RunPod14 min read
Building a High-Performance and Portable vLLM Linear Backend with Helion
TL;DR We integrated Helion into vLLM’s linear backend to explore how an autotuned, high-level kernel DSL can improve LLM inference performance while reducing kernel implementation complexity. A single Helion general...
PyTorch Blog13 min readAstaBrief 8B vLLM Local Deployment for Private RAG
Run AstaBrief 8B with vLLM and connect it to a private PDF retrieval pipeline, with citation IDs, prompt format, and validation steps.
Nano Imagine Blog7 min read
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