What local LLM should I use for autocompletion in a 2026 coding setup?

At its core, modern code autocompletion works by using an AI model to predict the next block of code as you type. The editor displays this prediction as inline "ghost text" directly at your cursor. If the suggestion is correct, you can accept it (usually by pressing Tab) to instantly insert the code, or ignore it (by pressing Esc or continuing to type) to dismiss it.

Currently, the best autocompletion option is GitHub Copilot, which offers high accuracy and deep contextual awareness. However, it is a paid subscription service and sends your source code to external servers, making it a compliance risk for proprietary environments.

The alternative is running an AI completion model locally on your own machine. The problem is that the standard recommended options in this space, such as Qwen2.5-Coder and StarCoder2, are now two years old (released in 2024). In AI development, relying on two-year-old weights means missing out on recent training techniques, better context handling, and improved code logic.

What are FIM models and how they differ from the chat/agentic models

You cannot just plug a standard chat model like LLaMA-3 into your IDE and get code completion. Chat models are causal: they read left-to-right and predict only the next token. If your cursor is in the middle of a file, a causal model cannot see any of the code written below the cursor. It is blind to your existing return statements, helper functions, and closing brackets, leading it to output duplicate code that breaks compilation.

To solve this, autocomplete engines require Fill-in-the-Middle (FIM) training. A FIM model is trained to condition on both a prefix (code before the cursor) and a suffix (code after the cursor), generating only the missing code in the middle. Special boundary tokens structure the input:

<|fim_prefix|>const x = 10;
[CURSOR HERE]
<|fim_suffix|>return x * 2;

By reading in both directions, the model inserts suggestions that fit the surrounding code.

How we tested newer CellSense models to find option for local autocompletion

To find a fresh local alternative to the older 2024 baselines, we turned to the CellSense-FIM series, a community fine-tune based on the Qwen2.5-Coder architecture, optimized specifically for modern, repository-aware FIM completions.

We subjected four parameter tiers (0.5B, 1.5B, 3B, and 7B) to 30 manual test cases across Python, C++, and TypeScript. We evaluated each model on suffix lookahead and stop token discipline (stopping output immediately when the gap is closed).

Here is the functional completion accuracy across parameter sizes:

We also clocked average bare-metal inference latency on a standard 6-8GB VRAM setup (using GGUF Q8_0 quantizations):

Evaluating the parameter tiers: finding the sweet spot

Finding the right model requires balancing size (VRAM footprint), speed, and accuracy:

• 0.5B (Lightweight but Weak): At only 1.2GB of VRAM and a blazing-fast 80ms latency, it is incredibly light on resources. However, at 53.3% accuracy, it fails to handle nested scopes or type signatures, requiring constant manual edits.
• 3B (Unstable Middle): Consuming around 6.5GB of VRAM and responding in 305ms, the 3B model seems like a logical compromise. But in our benchmarks, it scored 73.3% accuracy, lower than the 1.5B model. It suffered from Context Duplication, where it generates correct code but then repeats closing brackets and semicolons already present in the suffix, which breaks compilation.
• 7B (Capable but Heavy): Offering the highest accuracy at 86.6%, the 7B model understands complex architecture. But it requires 15GB of VRAM (hogging system memory) and has a 708ms latency, which creates a noticeable lag that disrupts typing.
• 1.5B (The Goldilocks Winner): Consuming just 3.5GB of VRAM and returning suggestions in 230ms, it sits comfortably within your system's resources. With an accuracy of 78.3%, it remains highly disciplined and terminates cleanly on stop tokens.

What didn't work: low quantizations and 7B bloat

We tried running several alternative setups that failed to balance these three axes:

• Low-bit Quantization (IQ2 and Q3): We attempted to run the 7B model using highly compressed 2-bit and 3-bit GGUF weights to fit it inside smaller VRAM profiles. While it reduced latency, it ruined the model's logic, causing severe Stop Token Amnesia where the model would write correct code and then keep generating endless boilerplate and UI components.
• High-parameter models on single GPUs: Running the CellSense 7B model at bf16 precision yielded high accuracy but hogged the GPU completely, causing lag in the IDE's UI rendering and web browser preview.

Setup: getting the local autocompletion loop running

To get the model running in your editor, you have two different options for VS Code: using the dedicated llama-vscode extension, or using the Continue extension with a manual llama.cpp server.

Option 1: The easy way (llama-vscode)

The simplest method is to install the official llama-vscode extension (see setup docs). It automatically downloads and manages a bundled llama.cpp instance for you, removing the need to configure a local server manually. You simply provide the path to your downloaded cellsense-fim-1.5b-Q8_0.gguf (available on Hugging Face) file in the extension settings, and it handles the rest.

Option 2: The power-user way (Continue + llama.cpp)

If you are already using the Continue extension for chat, you can run a manual llama-server and point Continue's autocomplete feature to it.

+------------------+                   +------------------------+
|   VS Code        |  1. Cursor Pause  |   llama-server         |
|   (Continue)     |------------------>| (Local OpenAI-style)   |
|                  |                   |                        |
|                  |<------------------| (Runs CellSense-FIM)   |
|   Ghost Text     |   4. Returns      +------------------------+
+------------------+     Middle                  |
        ^                                        | 2. Prefix & Suffix
        |                                        v
        |                              +------------------------+
        +------------------------------|    CellSense-FIM       |
                3. Generates Middle    | (Inference on VRAM)    |
                                       +------------------------+

1. Download and run llama-server

Obtain the llama-server binary (or use the llama serve CLI) from the llama.cpp releases. Instead of downloading the file manually, you can directly pull the model from Hugging Face:

# Start the server and offload all layers to your GPU
llama serve -hf arun11karthik/cellsense-fim-1.5b-GGUF:Q8_0 \
  -ngl 99 \
  -c 4096 \
  --port 8080

2. Configure Continue in VS Code

Install the Continue extension. Here is my continue config file:

name: Main Config
version: 1.0.0
schema: v1
models:
  - name: Cellsense
    provider: llama.cpp
    model: qwen2.5-coder
    apiBase: http://127.0.0.1:8080
    roles: [autocomplete]
    completionOptions:
      stop:
        - "<|endoftext|>"
        - "<|file_sep|>"
        - "<|fim_pad|>"

VS Code will now request completions from your local server as you type.

Limitations: what a local 1.5B model won't do

While the 1.5B model balances resources, accuracy, and speed well, it has clear limits:

• No Multi-File Context: Unlike cloud-scale completions, this local setup is limited to the active file prefix and suffix.
• Complex Architectural Design: A 1.5B model is excellent at completing logic blocks, but it cannot design complex systems or write multi-file features. It is a utility for writing code, not a software architect.

The verdict: run the 1.5B model

When setting up a local coding assistant, do not choose models based on parameter size or latency alone. The 7B model is too heavy for daily multi-tasking setups, and the 3B model is prone to scope duplication.

The CellSense-FIM 1.5B model at Q8_0 quantization is the optimal choice: it delivers 78.3% accuracy in a clean 230ms window while leaving your system's resources free to run your actual application.