Local LLM in VSCode: The 2026 Setup Guide

Running a large language model locally and wiring it into VSCode as a coding assistant is now a mainstream setup rather than a hobbyist experiment. The pieces are stable: a local inference server (usually Ollama) exposing an OpenAI-compatible API, and a VSCode extension that points at that local endpoint instead of a cloud provider. This guide covers the extension landscape, the setup steps, model choices, and — most importantly — the hardware math that decides whether the experience feels instant or unusable.

Why run an LLM locally inside VSCode

The three recurring motivations, per developer discussion on r/LocalLLaMA and r/buildapc, are privacy (code never leaves the machine), cost (no per-token API billing once hardware is paid for), and offline reliability (no outage risk from a third-party API). The tradeoff is that local model quality and speed are capped by what's sitting in your GPU's VRAM — a constraint cloud models don't have.

Best VSCode extensions for local LLM coding

Continue is the default recommendation for most individual developers because it's open source, actively maintained, and treats local models as first-class citizens rather than an afterthought bolted onto a cloud-first product. Cody and Tabby matter more once you're standing up a shared inference box for a team rather than running a model on each developer's own workstation.

Setting up Ollama as the local backend

1. Install Ollama from ollama.com/download — it runs as a background service and exposes an OpenAI-compatible API on http://localhost:11434/v1, which is what lets VSCode extensions treat it like any other OpenAI-style provider.
2. Pull a coding-tuned model — ollama pull qwen2.5-coder:7b (or a size appropriate to your VRAM; see the hardware section below).
3. Install Continue from the VSCode Marketplace and point its config at the local Ollama endpoint instead of a cloud API key.
4. Set separate models for autocomplete vs. chat if your hardware supports it — a smaller, faster model for inline suggestions and a larger one for multi-file chat, switched in Continue's config file.
5. Verify the round trip by triggering an inline suggestion and confirming in Task Manager / nvidia-smi / rocm-smi that the GPU (not just CPU) is doing the work — a common setup mistake is a driver mismatch that silently falls back to CPU inference.

Choosing a model for coding

Coding-tuned model families (Qwen2.5-Coder / Qwen3-Coder, DeepSeek-Coder, and coding-finetuned Llama and Gemma variants) consistently outperform general-purpose chat models of the same parameter count on completion and refactor tasks, per community benchmark discussion on r/LocalLLaMA. Two variables decide which one fits your machine: parameter count (roughly proportional to reasoning quality) and quantization level (how much that count is compressed to fit in VRAM). SpecPicks' Gemma 4 tool-calling fix and Gemma 4 stealth update writeups track how fast this landscape shifts — model releases and quantization fixes change the calculus every few weeks, so it's worth re-checking before committing to one model for a long-term setup.

Hardware requirements — what actually gates you

VRAM capacity, not raw compute, is the first wall most people hit. A model that doesn't fit in VRAM either won't load or spills into system RAM, which tanks throughput. Rough tiers, cross-referenced against SpecPicks' hardware coverage:

If a full desktop GPU build isn't in the budget, a compact option is worth considering — see SpecPicks' best mini PC for local LLMs comparison of a Ryzen AI Halo unified-memory box against a DIY 3060 build. For anyone weighing whether a specific newer open model will even fit their existing card, the Kimi K3 VRAM math piece walks through the same sizing logic applied to a larger model.

CPU vs. GPU inference — what changes in practice

CPU-only inference works but is dramatically slower for anything beyond short completions, since token generation is heavily parallel and GPUs are built for exactly that workload. The practical effect inside VSCode: CPU-only inline completions introduce enough latency that they stop feeling like autocomplete and start feeling like a chat response, which breaks the workflow they're meant to support. This is the single biggest reason local-LLM VSCode setups live or die on GPU VRAM rather than CPU core count.

For AMD hardware specifically, Ollama's ROCm support has matured but coverage varies by GPU generation — check current compatibility on Ollama's documentation before assuming a given AMD card will accelerate inference the same way an equivalent NVIDIA CUDA card would.

Troubleshooting common issues

• Extension shows "model not found": confirm the exact model tag (ollama list) matches what's configured in the extension — tags are case- and version-sensitive.
• Suggestions feel slow: check whether inference is actually running on GPU (driver/runtime mismatch is the most common cause of a silent CPU fallback).
• Out-of-memory errors on load: drop to a more aggressive quantization (e.g., Q4 instead of Q8) or a smaller parameter count before assuming the GPU itself is the problem.
• Completions are technically correct but unhelpful: try a coding-tuned model instead of a general chat model — the gap in code-specific benchmarks is consistently large even at the same parameter count.

Bottom line

A local LLM in VSCode is a solved workflow in 2026 — Ollama plus Continue (or Cody/Tabby for team setups) covers the vast majority of use cases with no cloud dependency. The remaining variable is entirely hardware: VRAM capacity decides which models are usable, and GPU acceleration decides whether the experience feels like real-time autocomplete or a laggy chatbot. Match the model size to the VRAM tier above before troubleshooting the extension itself.

Citations and sources

• Ollama download
• Ollama OpenAI-compatible API announcement
• Continue documentation
• Continue VSCode Marketplace listing
• Sourcegraph Cody
• Tabby (TabbyML) GitHub repository

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.