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Articles, guides, and insights on local artificial intelligence
13 articles found per "ollama"
Yzma: embeddings up to 2.6x faster than Ollama, the comparison
Yzma calls llama.cpp in the same process: no server, no round trip. It is 2.6x faster than Ollama on embeddings — with the numbers, the math and the setup.
ML Drift: Google's open-source on-device GPU engine, and when it beats the generalists
The engine that runs models inside Chrome and YouTube is now open source: architecture, benchmark numbers, and an honest comparison with Ollama and llama.cpp.
ML agents that run locally: MLExpert and EpochGo on Ollama, zero API costs
MLExpert raises the bar to 35B and a 72.7% medal rate; EpochGo runs three AutoML agents on 16 GB at zero API cost: what works today, the math and the limits.
MiniCPM-V 4.7: the 35B MoE that runs on a smartphone with 3 GB of RAM
MiniCPM-V 4.7 (35B total, ~3B active, released October 12, 2026 by OpenBMB and ModelBest) is the first open MoE in the series, vision-language: multiple images, video, OCR. 13+ tok/s with 3 GB of RAM on an iPhone 17 Pro Max, 17+ on Android. Official quantizations BF16, FP8, INT4, AWQ, GPTQ, GGUF; engines Ollama, llama.cpp, vLLM, SGLang. The three techniques: Lookahead Expert Scheduling, fine per-level quantization, DSpark speculative sampling.
Aleph Alpha's Kolibri: the 78B MoE that runs (almost) locally
Aleph Alpha's Kolibri (78.1B/3.46B, Apache 2.0, FP8) is the European open-weight MoE: reasoning, tool calling and context up to 1M tokens. In FP8 it weighs ~78GB of VRAM: it runs on an RTX 5090 32GB + shared RAM or on unified-memory Macs; for practical adoption the kolibri1 architecture in llama.cpp and Ollama/LM Studio builds are still missing.
Ollama vs llama.cpp: under the hood
Ollama runs llama.cpp under the hood, but the two are not the same thing: one is the engine, the other the car. What's really underneath, when the difference matters, and how to choose without slogans.
Privacy: what actually goes over the network when AI is local
Your data doesn't leave the machine — almost. Here's what actually goes out over the network with Ollama and LM Studio, and how to verify it in half an hour with tcpdump, Wireshark and mitmproxy.
A Model Without a GPU: When the CPU Is Actually Enough
In 2026, CPU-only inference is no longer a fallback: when it genuinely works, what speed to expect, and which levers to pull to squeeze the most out of your processor.
GPU or CPU: when the CPU is enough
How prefill and decode work, what matters for speed (memory bandwidth), real numbers on a mainstream laptop, the cases where the CPU is enough and where VRAM is needed — with the commands to measure.
Open WebUI: the interface for your local AI
Open WebUI is the full web interface for your local models: Docker install, connecting Ollama, per-model customization, document RAG, and its honest limits.
Local AI on Mac: the complete Apple Silicon guide
Why Apple Silicon (unified memory, GPU cores) is the simplest machine for local AI: the RAM you need, installing Ollama, recommended models per chip generation, and realistic performance expectations.
Running LLM Models Locally: From First Install to First Working Model
Learn how to install and use large language models on your own computer, without relying on cloud services.
Ollama vs LM Studio: Which Tool to Choose
Ollama for servers and scripts, LM Studio for desktop exploration: the comparison table, the two APIs (11434/1234) and the real case of using both.