AI Ram Ollama Continue Dev

A llamafile is just a single, executable file that bundles the llama.cpp engine with model weights.

However, for a 30GB+ model like our Q8_0 GGUF, creating a 30GB executable is impractical. The real power-user workflow, which perfectly suits your goal, is to use the llamafile executable as a portable server and tell it to load an external GGUF file.

This gives you the best of all worlds:

Portability: A single, ~15MB llamafile executable that you can drop on any (Linux/macOS/Windows) machine.
Power: You can load any GGUF file you want, including our 30GB gemma-3-27b-it-q8_0.
Performance: You can pass all the llama.cpp performance flags (--mlock, --threads, --n-gpu-layers) directly to it.
Customization: You can apply LoRA adapters on the fly.
Integration: It starts an OpenAI-compatible server, which is exactly what tools like Continue.dev need.

Here is the complete walkthrough to create the ultimate, portable, high-performance coding experience.

## The "Portable Powerhouse" `llamafile` Walkthrough

### Phase 1: Acquire Your "Engine" and "Fuel"

We need two things: the llamafile executable (the engine) and our high-fidelity model (the fuel).

Download the llamafile Executable: Go to the llamafile GitHub releases page and download the main llamafile-0.8.6 (or newer) executable. We don't need a model bundled with it.

# Create a workspace
mkdir -p ~/llamafile-power-setup
cd ~/llamafile-power-setup

# Download the llamafile binary
wget https://github.com/Mozilla-Ocho/llamafile/releases/download/0.8.6/llamafile-0.8.6

# Make it executable (Linux/macOS)
chmod +x llamafile-0.8.6

# On Windows, you would just rename it to "llamafile-0.8.6.exe"

You now have your portable engine.

Download the High-Fidelity GGUF Model: This is the same as our previous step. We'll download the 30GB Q8_0 model and place it in a models folder.

# Install the tool to download from Hugging Face
pip install -U huggingface-cli

# Create a models directory
mkdir -p ./models

# Download our 30GB Q8_0 model
huggingface-cli download \
  paultimothymooney/gemma-3-27b-it-Q8_0-GGUF \
  gemma-3-27b-it-q8_0.gguf \
  --local-dir ./models \
  --local-dir-use-symlinks False

### Phase 2: Launch the Server with Max Performance Flags

This is the core of the setup. We will run our llamafile executable and pass it all the high-performance llama.cpp flags.

First, find your PHYSICAL core count (e.g., sysctl -n hw.physicalcpu on macOS or lscpu | grep "Core(s) per socket" on Linux). We'll use 8 cores as our example.

Here is the full launch command:

# Run this command from your '~/llamafile-power-setup' directory
# This launches the OpenAI-compatible server

./llamafile-0.8.6 \
    # --- Server Flags ---
    --server \
    --port 8080 \
    --host 0.0.0.0 \
    
    # --- Model Flags ---
    -m ./models/gemma-3-27b-it-q8_0.gguf \
    -c 131072 \
    
    # --- Performance Flags (CPU/RAM/GPU) ---
    # --mlock: Force model into RAM. CRITICAL for performance.
    --mlock \
    
    # -t: Thread count. Set to your PHYSICAL core count.
    -t 8 \
    
    # -ngl 99: Offload 99 layers to the GPU.
    # If you have a GPU, this is essential.
    # If you are CPU-only, set this to 0 (or just omit it).
    --n-gpu-layers 99

Your terminal will now show server logs. You have a high-performance, OpenAI-compatible API running at http://127.0.0.1:8080.

### Phase 3: Customize for Code (Apply LoRA)

This is what makes the llamafile server so powerful. You don't need to build a new model. You can "hot-swap" a LoRA by just adding a flag at launch.

Let's assume you've downloaded a rust-code-lora.gguf into your ./models folder.

You would simply add one flag to the launch command:

./llamafile-0.8.6 \
    --server \
    --port 8080 \
    -m ./models/gemma-3-27b-it-q8_0.gguf \
    -c 131072 \
    --mlock \
    -t 8 \
    --n-gpu-layers 99 \
    
    # --- The LoRA Customization ---
    --lora ./models/rust-code-lora.gguf

Now, the server running at http://127.0.0.1:8080 is serving your gemma-q8 model already specialized for Rust. You can have multiple scripts to launch different "specialist" servers.

### Phase 4: Integrate with `Continue.dev` for the Best Code Experience

This is the final step. We will point Continue.dev at our new, high-performance llamafile server.

Continue.dev can connect to any OpenAI-compatible API.

Open VS Code and go to your ~/.continue/config.yaml file.

Paste in this configuration:

models:
  - name: "llamafile-coder"
    title: "Gemma 3 27B Coder (llamafile)"
    
    # We use the "openai" provider, as llamafile emulates it
    provider: "openai"
    
    # This is just a label for the API, it can be anything
    model: "gemma-3-27b-it-q8_0" 
    
    # --- The Integration ---
    # Point to your local llamafile server
    apiBase: "http://127.0.0.1:8080/v1" 
    
    # The API key can be any non-empty string
    apiKey: "llamafile" 

  - name: "Local Embedder (SOTA)"
    title: "MixedBread Embed Large"
    provider: ollama
    model: "mxbai-embed-large-v1:latest" 
    apiBase: "http://localhost:11434"
    embed: true

# --- Set your models as default ---
modelRoles:
  chat: "llamafile-coder"
  edit: "llamafile-coder"
  
embeddingsProvider: "Local Embedder (SOTA)"

# ... (rest of your contextProviders config) ...

(Note: This setup still uses your Ollama server for embeddings, as it's the simplest way to manage the mxbai-embed-large model. Your llamafile server will handle all the generation.)

Reload VS Code.

You are now 100% operational. When you type @codebase in Continue.dev:

Continue.dev uses your Ollama mxbai-embed-large model to index your code.
It finds the relevant code snippets.
It sends the prompt and code context to your llamafile server at http://127.0.0.1:8080.
Your llamafile server, running with locked RAM and full GPU/CPU acceleration, generates the code response using the high-fidelity gemma-q8 model (with the Rust LoRA, if you added it).

You have successfully combined the raw power of a natively-run llama.cpp engine with the simplicity of a llamafile server and the deep integration of Continue.dev.

#AI Ram Ollama Continue Dev

### The "Portable Powerhouse" llamafile Walkthrough

#### Phase 1: Acquire Your "Engine" and "Fuel"

#### Phase 2: Launch the Server with Max Performance Flags