PC for vibe coding and multiple agents: what to assess

1. When vibe coding, separate the AI response from running the project

In this guide, vibe coding means developing with the help of natural-language instructions to an AI. During the session, note when you are waiting for a response, installing dependencies, building or running tests. If only the remote response is slow and the editor remains responsive, check the service and connection before blaming local hardware. If the entire project becomes unresponsive, watch processes and memory while the task runs.

• Record the project, tool, where commands execute and which tasks overlap.
• Repeat a build or test of the same project; compare completion time and responsiveness while keeping other conditions the same.
• Review and test the generated code: low resource usage does not prove that the program is correct or secure.

• Ollama — cloud-hosted models
• LivoPC Monitor — features and availability

2. Map where each task takes place

An editor installed on your PC can use a remote model; an agent can also execute commands locally or in a remote environment. Check the tool's configuration. Ollama, for example, documents both local execution and cloud models. Do not size your PC's GPU for inference that runs on another server.

• Ollama — cloud-hosted models

3. Size for the task, not the number of windows

Note which builds, tests, servers and databases remain active at the same time. In Docker Desktop, CPU, memory and disk limits depend on the platform and backend; with WSL 2, some of these settings belong to the WSL VM. A configured limit is not proof of a physical hardware shortfall. Change only settings you understand and record the change for comparison.

• Docker — Docker Desktop resources and settings

4. Observe comparable sessions before buying

Use the same project and a representative sequence. The procedure below helps you observe your own usage; the type-checking experiment later shows a limited application of it. Record what you started manually: aggregate metrics do not identify which agent or app caused a change.

• Note the CPU, installed RAM, storage, operating system and relevant versions; record whether the PC is plugged in and its power mode.
• Define the task, data, duration and completion criterion before starting; keep them the same across comparisons.
• Observe a typical run. Then change only concurrency or the selected limit and repeat.
• Record perceived delays, load and memory throughout the session. Distinguish peaks, gaps and unavailable readings.
• Use system tools to investigate processes. On a Mac, memory pressure complements the total used; do not apply the same threshold to every operating system.

• Apple — memory and memory pressure in Activity Monitor

5. Educational example: two routines, one testable question

Consider a web project using an editor, browser, containerized database and tests. In routine A, tests from two tasks overlap; in B, you run them sequentially. Compare both with the same project and record the time you observe. If the experience changes, that warrants investigating concurrency; it does not prove that buying a CPU or RAM will produce the same result. This container scenario is hypothetical; the only real timings in this guide come from the type check described below.

• Docker — Docker Desktop resources and settings

6. Real observation: two checks of the same project

On September 29, 2026, we observed two type checks of the LivoPC project in the same local environment: Intel(R) Core(TM) i3-N305, 7.68 GiB of RAM reported by the system, Windows 10.0.26200 x64, Node v24.14.1 and TypeScript 6.0.3. This is a real development task, with no agents, AI models or containers running. The table preserves each repetition, including its variation.

• Command per task: node node_modules/typescript/bin/tsc --noEmit --incremental false. Two runs per routine, both completed with exit code zero.
• We discarded one warm-up run and disabled the incremental cache. The order was sequential, parallel, parallel and sequential, with two repetitions of each routine.
• Total time runs from the start until both tasks finish. CPU is a system-wide average over the interval, including other machine activity; it is not usage attributed to the checks.
• Shared environment: disk cache, power, temperature and external activity were not controlled. Storage and GPU were not identified. Free RAM in the report does not measure pressure or per-task peaks.
• Variation between repetitions prevents claiming a stable gain. One machine and two repetitions do not establish ideal RAM, a bottleneck, productivity or the number of agents supported.

• LivoPC — type-checking protocol and results

• View the observation record, conditions and limitations (JSON)

7. If the model is local, start a second check

In Ollama, concurrency depends on available memory, and context and parallel requests change its requirements. Requests may queue even when nothing is wrong with the PC. Identify the exact model, format, quantization and context before comparing hardware. File size does not represent all runtime memory.

• Ollama — concurrency, memory and queues

• Investigate slow Ollama or LM Studio performance

8. Decide between reorganizing the workflow and changing the machine

If running tasks sequentially already meets your needs, record that alternative. If a limitation recurs in a necessary task, confirm the component and whether it can be upgraded. For a laptop, check the exact variant and manual: soldered memory, slots and cooling limits cannot be inferred from its marketing name. Also compare mobility, monitor and the time needed to migrate your environment.

• Docker — Docker Desktop resources and settings
• LivoPC Monitor — features and availability

9. Use LivoPC to keep the context behind your decision

On Windows, use LivoPC Monitor to observe resources during a build or development session. The local process list helps compare resource consumers; it does not automatically identify agents or measure the speed of a remote model. CPU, RAM, GPU and available temperatures provide context for the symptom. You can also record your configuration and plan a change after confirming the need.

• LivoPC Monitor — features and availability

• LivoPC Monitor — features and availability