Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsI would start with a workstation that runs the model runtime and stores the model locally, then connect the coding tools I actually need. That can keep inference prompts and responses on the machine, but it does not automatically make an IDE, extension, agent, plugin, model download, or network connection private. Because the right hardware depends on the model, operating system, workload, and budget, there is no responsible one-size-fits-all parts list.
What “private” means for a coding workstation
A local coding setup has several separate parts: the IDE, its assistant extension or agent, the model runtime, the model files, and any tools or services the agent can call. Keeping inference local addresses one important data path—the prompts sent to the model and its replies—but it does not establish how every other component handles code, telemetry, credentials, or network requests.
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Ollama’s FAQ says of its local runtime: “No. Ollama runs locally, and conversation data does not leave your machine.” Treat that as a statement about Ollama running locally, not a blanket guarantee about an entire editor-and-agent workflow. Check the current behavior and policies of each component you enable.
Keep the first network boundary simple
Ollama documents a default server bind of 127.0.0.1:11434, which makes the endpoint available on the local machine. Its OLLAMA_HOST setting can change the bind address. I would leave the endpoint local initially; changing it to make the service reachable from other devices is a deliberate change to the exposure boundary, not just a convenience setting.
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Model acquisition is another distinct step: downloading a model requires a network connection, even if later inference happens locally. A local inference path should not be confused with an offline setup or a guarantee that no other workflow component communicates externally.
Choose the hardware path by workload, not headline specs
There are two practical architectures to consider: Apple Silicon systems with unified memory and workstations with a discrete GPU. Both can be sensible; the sources available here do not provide a comparable independent benchmark that establishes a universal winner.
| Path | What the cited guidance establishes | What to verify for your workload |
|---|---|---|
| Apple Silicon unified memory | OpenJet recommends Apple Silicon with 24 GB or more unified memory for its managed terminal agent; this is a vendor configuration recommendation, not an independent benchmark. Ollama’s MLX preview instructions request a Mac with more than 32 GB unified memory for the Qwen3.5-35B-A3B example. Ollama’s page describes a test dated 2026-03-29. | Confirm the exact model artifact, quantization, context length, runtime support, and memory headroom. The two memory figures refer to different software and model paths, not competing universal minimums. |
| Discrete-GPU workstation | OpenJet recommends a GPU with 14 GB or more VRAM for its managed local runtime. NVIDIA PAIR lists GeForce RTX 20 Series or newer and RTX PRO Turing or newer among supported hardware families. These recommendations do not identify the best retail GPU for an individual coding workload. | Check model memory needs, operating-system and runtime support, GPU fit in the case, power-supply capacity, cooling, noise, and budget before choosing a card. |
Size for the actual model and context
Parameter count alone is not a useful purchase rule. Before spending on hardware, identify the exact model artifact and quantization, then account for the model’s file size, RAM or VRAM, memory bandwidth, context length and KV cache, runtime offload behavior, other software using memory, and sustained cooling. Leave headroom for the operating system and the editor rather than assuming all installed memory is available to inference.
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OpenJet’s configuration table includes model-specific memory targets—for example, 20 GB for its Qwen3.8 27B Q4_K_M MTP setup. That is a configured target for that application setup, not an independent performance measurement or a general hardware requirement.
- Decide which coding tasks and context lengths matter to you.
- Check the chosen runtime’s support for your operating system and intended memory path.
- Determine whether the model can fit with useful context and KV-cache headroom.
- Consider sustained performance, noise, power, physical size, and upgrade options alongside capacity.
A community Local AI Workstation Guide, last reviewed 2026-08-10, also cautions against treating a low-spec example or a model’s parameter count as proof of universal smoothness. Its Windows-first example uses Windows, WSL2, Docker, Ollama, and Open WebUI as a staged architecture; use current official project documentation for installation instructions rather than assuming community steps remain current.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build the simplest useful local architecture first
I would begin with one workstation and one locally stored model, avoiding extra network hops until there is a clear need for them.
- Choose the host: Select an Apple Silicon or discrete-GPU path based on the model and runtime you intend to use, not a generic claim about which platform is fastest.
- Install a local runtime: Configure it to serve inference on the workstation. Ollama’s documented local default is
127.0.0.1:11434. - Acquire a specific model: Record the exact model and quantization, and verify its memory and context requirements against the machine.
- Connect the editor or agent: Configure the integration to use the local runtime where supported, then check whether the extension or agent sends other data to remote services.
- Review tool access: Treat shell execution, repository access, browser or network tools, embeddings, telemetry, and credential handling as separate privacy decisions.
Ollama’s FAQ covers editor and plugin use, but an editor integration does not by itself prove that every feature operates locally. Verify the data path for the exact extension and features you enable.
When multiple machines help—and when they do not
If you already have several trusted systems, routing separate inference requests can increase the number of machines available to do work. NVIDIA PAIR provides Ollama-compatible and OpenAI-compatible proxy endpoints for this kind of routing. Its documentation, last updated 2026-08-17, says each request is sent to one eligible system; the app accepts requests only from the local system and calls for a trusted local network when pairing.
That arrangement does not pool the machines’ memory to fit one larger model: PAIR explicitly says it does not combine GPU memory, join GPUs into a larger GPU, or split a model or request across systems. It can route independent requests, but it will not make a model that exceeds one machine’s memory fit by aggregating the others.
Use a hybrid architecture only when its governance fits
A local workstation is not the only architecture available to a team. AWS’s public-sector reference architecture describes IDE plugins connecting to approved model providers, with optional autocomplete and embeddings hosted locally and larger chat workloads handled by managed or self-hosted model services. That is a governed hybrid or on-premises pattern, not evidence that a personal workstation is offline or that hosted inference has the same privacy boundary as local inference.
What I would decide before buying
I would defer a specific machine or GPU recommendation until the model, operating system, budget, code workload, and acceptable generation speed are known. No independent comparison cited here establishes a best-performing or best-value choice for an unspecified task set.
- Model fit: Can the exact artifact and quantization fit with the context length you need?
- Runtime support: Does the chosen runtime support the model and intended operating system or accelerator?
- Memory headroom: Is capacity sufficient for the model, KV cache, editor, and other concurrent software?
- Sustained operation: Can the system cool the workload without unacceptable noise or power use?
- Practicality: Are size, upgrade path, initial cost, and shared use by other tools or people acceptable?
- Data paths: Have you checked the IDE, extension, agent tools, runtime, model acquisition, telemetry, and network routing separately?
OpenJet’s memory figures and the Ollama MLX example are useful starting points only for their respective setups. Confirm current requirements for the exact model and runtime before purchasing; the available sources do not establish current prices, comparative buying results, or a universal best coding model.
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