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LM Studio is a desktop app for downloading and running large language models locally on a user's own hardware, with a chat interface and local API server.
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Llama 8B on an RTX 4060 Ti 16GB fell from 42.5 to 3.8 tokens a second with a fifth of the model in system RAM, according to a dev.to benchmark. For a local coding agent, that makes VRAM for weights plus context the first spec to check, ahead of bandwidth.
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WorldScript Studio's writing app stays fully usable with no API key, model or network because every AI feature reaches providers through one service. That leaves one module for a test suite to check when a provider goes down.
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PAIR proxies Ollama and LM Studio, so the agent keeps seeing one connection and no harness code changes. The adoption cost moves to disk, because a node is only eligible if it already has the exact model downloaded.
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LM Studio 0.4.0 shipped llmster, a headless daemon that runs it on the Linux GPU servers where MIT-licensed Ollama already worked. Teams whose policy demands auditable source now decide on licence, since only LM Studio's lms CLI carries an MIT grant.
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Z.ai's 320-billion-parameter model activates 18 billion per token and ships under MIT, so a buyer can download it and measure for themselves. Every capability figure published so far comes from Z.ai's own launch materials.
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One MIT-licensed proxy on localhost is enough to serve OpenAI's own desktop client from self-hosted models. The models that fail there fail on Codex's tool-call format. One of three tested got lost.
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Every local runner now reads the same GGUF file, so the binding decision is the quant tag and the gigabyte or two of context that has to fit beside it. Ollama sets the GPU offload itself; llama.cpp lets you set it.
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Serge Kernbach's dev.to build notes put a 9B to 27B assistant on two RTX 4070s totalling 24 GB and argue that integration beats raw model quality. The figures he publishes are power draw and PCIe bandwidth.
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A dev.to guide puts the llama.cpp and Ollama decision on ownership. Under Ollama the named model is the unit you operate; under llama.cpp it is the llama-server process and the seven flags in its command line.
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ROCm sits under PyTorch, vLLM and SGLang; Vulkan is what llama.cpp-class engines compile shaders against. The engine you already run narrows the choice to one, and the rest of the work is reading logs.
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Apple says the M5 Ultra reaches 512GB of unified memory at 1.2TB/s, which settles how big a local agent can get, though the September launch arrives without prices and every AI speed figure is measured against an M1.
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PAIR cut a five-subagent inbox task from 18 minutes to 8 minutes 48 seconds across three machines, which is just over two times the speed for three times the hardware, and every box in NVIDIA's demo cluster was one NVIDIA sells.
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Nvidia's Personal AI Router, shown at IFA 2026, farms an agent's subtasks out to whichever household machines happen to be asleep, which buys speed on unattended jobs at the cost of any promise about when they finish.
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Nvidia's Apache 2.0 beta seizes the port Ollama or LM Studio was using and forwards each call to whichever machine is free, which makes local throughput a function of how many PCs you own. Hands-on testing found it serving an engine Nvidia does not list.
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Treating the runtime and the weights as a container image buys you a hardened default docker run and a signable artifact. On Apple Silicon, though, that same boundary costs you the GPU. One hands-on run puts a number on that cost.
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Ollama, LM Studio, vLLM and Gradio all listen on well-known ports, and none of them ask for a credential unless you go and build one, so the one-line change that lets you test from your phone also answers the office subnet.
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The 2025 configurations are gone from the store rather than fading out, which turns a refresh most shops would have taken at renewal into a purchase they have to justify on this quarter's numbers.
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The published deltas put the Neural Engine at 2x and multithreaded CPU at 1.2x. Apple's own workload figures widen that gap further, and the lineup stops at base tier.
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The quad-die design breaks the recipe Apple has used for Ultra chips since M1. Interconnect goes to 4.4TB/s from 2.5TB/s, while the 512GB memory ceiling and the 80 GPU cores do not move.
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The bandwidth gap is 4.4x and the capacity gap is 4x, which is why these two boxes are not really competing. One decides whether a model fits; the other decides whether it is usable.
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