How to Install Devstral 2 Local AI Coding Assistant: The Free Copilot Alternative

The landscape of AI-assisted programming has fundamentally shifted in the past 18 months. What was once the exclusive domain of cloud-based services costing hundreds of dollars annually can now run entirely on your local machine—faster, more secure, and without any recurring fees. This isn't a compromise or a "good enough" solution; for many developers, local Large Language Models (LLMs) have become the superior choice.

In this comprehensive Tekin Gaming guide, we'll walk you through installing Devstral 2 (a Mistral-based coding model) using Ollama, integrating it with Visual Studio Code, and optimizing it for professional development workflows. Whether you're a solo developer concerned about code privacy, a startup avoiding cloud vendor lock-in, or simply someone tired of paying monthly subscription fees, this guide will show you how to reclaim control over your AI coding assistant.

Chapter One: The Case Against Cloud-Based Coding Assistants

Before we dive into the technical implementation, it's crucial to understand why an increasing number of professional developers are migrating away from services like GitHub Copilot, ChatGPT Plus, and Claude Pro. The reasons go far beyond simple cost considerations.

The Security Nightmare Hidden in Convenience

Every time you accept a code suggestion from GitHub Copilot or paste a function into ChatGPT, that data traverses the public internet to reach servers owned by OpenAI, Microsoft, or Anthropic. For personal projects and open-source work, this might be acceptable. But consider these scenarios:

• Proprietary Business Logic: If you're building a fintech algorithm, a unique recommendation engine, or any competitive advantage encoded in software, sending that code to third-party servers creates an irrecoverable security risk. Even with assurances about data handling, you're trusting corporate policies that can change.
• Regulatory Compliance: Industries like healthcare (HIPAA), finance (SOX, PCI-DSS), and government contracting often have strict data residency requirements. Using cloud AI services may violate these regulations, exposing your organization to legal liability.
• Accidental Credential Exposure: Even careful developers occasionally paste code snippets containing API keys, database passwords, or authentication tokens. With cloud services, these secrets are transmitted and potentially logged.
• Intellectual Property Concerns: Some service agreements include clauses allowing the provider to use submitted data for model improvement. Your innovative code could theoretically end up training models that benefit your competitors.

With a Local LLM, none of your code ever leaves your machine. You can physically disconnect from the internet and your coding assistant continues functioning perfectly. For companies handling sensitive data, this isn't just a nice-to-have feature—it's often a legal requirement.

The Economics of Ownership vs. Rental

Let's run the numbers with brutal honesty. A professional developer using cloud AI services might be paying:

• GitHub Copilot: $10/month ($120/year)
• ChatGPT Plus: $20/month ($240/year)
• Claude Pro: $20/month ($240/year)

Total annual cost: $600 ($50 per month). Over three years, that's $1,800. Over five years: $3,000. And these are current prices—history shows subscription services tend to increase pricing over time.

Now consider the alternative: invest $400-700 in an NVIDIA RTX 3060 Ti (12GB) or RTX 4060 Ti (16GB). This hardware:

• Has zero recurring costs after purchase
• Provides unlimited inference—no rate limits or usage caps
• Serves multiple purposes: AI development, gaming, 3D rendering, video encoding
• Can be upgraded or resold, retaining some residual value
• Works regardless of internet connectivity or service availability
• Isn't subject to price increases, service discontinuation, or terms of service changes

The breakeven point occurs around month 8-12, depending on your hardware choice. After that, every month represents pure savings. Over five years, the financial advantage of local AI becomes overwhelming.

Performance and Control

Counterintuitively, local inference can actually be faster than cloud API calls in many scenarios:

• Zero Network Latency: Data moves from system RAM to GPU VRAM at gigabytes per second over PCIe, not through the internet at megabits per second. For short queries, this eliminates 100-300ms of round-trip time.
• No Rate Limiting: Cloud APIs impose strict limits—typically 3-5 requests per minute for free tiers, 60-200 for paid plans. Your local model accepts unlimited requests instantaneously.
• Guaranteed Availability: When OpenAI experiences an outage (which happens monthly), your work stops. Local models have 100% uptime as long as your computer runs.
• Complete Customization: You can modify system prompts, adjust temperature and sampling parameters in real-time, and even fine-tune models on your own codebase—something impossible with closed commercial APIs.

For developers working on large refactoring tasks that require hundreds of AI queries, the difference between rate-limited cloud access and unlimited local inference isn't just convenient—it's transformative.

Chapter Two: Hardware Requirements and Model Architecture

Running Large Language Models differs fundamentally from traditional software or even gaming. Understanding the hardware demands helps you make informed purchasing decisions and set realistic performance expectations.

Why VRAM is the Critical Bottleneck

Unlike gaming, where GPU compute power (TFLOPS) matters most, LLM inference is primarily memory-bound. The entire model must be loaded into VRAM before inference can begin. Here's the math:

A 7-billion parameter model stores each parameter as a numerical weight. Using different precision formats:

• FP32 (Full Precision): 4 bytes per parameter = 28GB VRAM required
• FP16 (Half Precision): 2 bytes per parameter = 14GB VRAM required
• INT8 (8-bit Quantization): 1 byte per parameter = 7GB VRAM required
• INT4 (4-bit Quantization): 0.5 bytes per parameter = 3.5GB VRAM required

Quantization reduces memory requirements by storing weights with lower precision. Modern quantization techniques (GGUF, GPTQ, AWQ) minimize quality loss—INT4 models typically retain 95-98% of the original model's capabilities while using one-eighth the memory.

This is why a 12GB GPU is considered the minimum for comfortable 7B model usage, while 24GB enables running 13B models or multiple smaller models simultaneously.

Can You Run This Without a Dedicated GPU?

Yes, but with significant compromises. CPU-only inference is possible using:

• Intel processors: AVX2 and AVX-512 instruction sets accelerate matrix operations
• AMD processors: Similar vector extensions provide modest speedups
• Apple Silicon: M-series chips with Unified Memory architecture perform surprisingly well, often matching entry-level discrete GPUs

However, expect token generation speeds of 2-8 tokens/second on CPU versus 20-80 tokens/second on a mid-range GPU. For learning and experimentation, this is acceptable. For daily professional use, it becomes frustrating quickly.

AMD Radeon RX 6000 and 7000 series GPUs are also supported through ROCm (Radeon Open Compute), though performance and compatibility lag behind NVIDIA's CUDA ecosystem.

Chapter Three: Understanding Ollama and Devstral 2

Just two years ago, running an LLM locally required expertise in Python environments, CUDA toolkit installation, model weight management, and API server configuration. Ollama changed everything by packaging this complexity into a single executable.

What is Ollama and How Does it Work?

Ollama is an open-source platform that manages the entire lifecycle of local LLMs. Think of it as Docker for AI models—it handles downloading, versioning, serving, and hardware optimization automatically.

When you run ollama run mistral, the following happens in under 60 seconds:

1. Model Discovery: Connects to Ollama's model registry and identifies the latest Mistral version
2. Intelligent Download: Pulls the optimal quantized version based on your available VRAM
3. Hardware Detection: Auto-detects CUDA/ROCm/Metal acceleration and configures accordingly
4. Memory Management: Loads model weights into GPU memory using efficient batching
5. API Server: Launches a REST API server on localhost:11434 compatible with OpenAI's API format
6. Interactive Shell: Provides a command-line chat interface for immediate testing

Ollama abstracts away complexity while remaining fully transparent—advanced users can override every default through Modelfiles (similar to Dockerfiles).

Why Devstral 2 / Mistral for Coding?

The open-source LLM ecosystem offers dozens of models: Meta's Llama 3, Google's Gemma, Microsoft's Phi-3, and specialized coding models like CodeLlama and DeepSeek Coder. Mistral (and its coding-focused variant Devstral) stands out for several reasons:

• Architecture Innovation: Uses Sliding Window Attention (SWA) for better long-context understanding than traditional transformers
• Efficiency: Matches or exceeds 13B models while using only 7B parameters through distillation techniques
• Multilingual Support: Trained on code repositories in 80+ programming languages
• Licensing: Apache 2.0 license permits commercial use without restrictions
• Active Development: Frequent updates from Mistral AI with performance improvements

While DeepSeek Coder achieves higher benchmark scores, Mistral/Devstral offers better general-purpose capabilities—useful when you need explanations, documentation, or non-code tasks. For pure code completion, DeepSeek is worth testing alongside Mistral.

Chapter Four: Step-by-Step Ollama Installation

Let's get hands-on. This walkthrough assumes Windows 10/11, but the process is nearly identical on macOS and Linux.

Step 1: Downloading and Installing Ollama

1. Navigate to Ollama.com in your web browser
2. Click the large "Download for Windows" button (macOS and Linux users will see appropriate versions)
3. The installer (~500MB) will download. Once complete, launch the executable
4. Accept the User Account Control prompt if it appears
5. Installation completes automatically in 15-30 seconds—no configuration dialogs
6. Look for the Ollama llama icon in your system tray (bottom-right on Windows, top-right on macOS)

That's genuinely it. No Python virtual environments, no CUDA driver manual installation, no PATH configuration. Ollama handles everything.

Step 2: Verifying Installation

Open a terminal window:

• Windows: Press Win + R, type cmd, hit Enter
• macOS: Press Cmd + Space, type terminal, hit Enter
• Linux: Press Ctrl + Alt + T

Type this command and press Enter:

ollama --version

You should see output like: ollama version 0.1.48

If you get an error like 'ollama' is not recognized, restart your terminal (or reboot your computer) to refresh environment variables. In rare cases, you may need to add Ollama to your PATH manually.

Chapter Five: Downloading and Running Your First Model

With Ollama installed, you're ready to download a model. We'll start with Mistral—the base model that Devstral is built upon.

Pulling the Model

In your terminal, execute:

ollama run mistral

This single command initiates a sequence of operations:

1. Registry Check: Connects to Ollama's CDN to fetch model metadata
2. Size Calculation: Determines optimal quantization level for your GPU (typically Q4_K_M for 8-12GB cards)
3. Download: Pulls approximately 4.1GB of model weights (time varies by connection speed—typically 5-30 minutes)
4. Verification: Checksums downloaded files to ensure integrity
5. Loading: Maps model into GPU VRAM (takes 10-30 seconds depending on hardware)
6. Initialization: Starts REST API server on port 11434
7. Shell Launch: Opens an interactive chat interface

Progress is displayed with a visual progress bar. On a 100Mbps connection, expect the download to complete in roughly 5-8 minutes.

Your First Interaction

Once the model loads, you'll see a prompt: >>>

Try this query:

>>> Write a Python function to calculate Fibonacci numbers using memoization

Watch as code materializes character-by-character. You're witnessing a 7-billion parameter neural network running entirely on your hardware, generating syntactically correct, idiomatic code without touching the internet.

To exit the chat interface, type /bye or press Ctrl + D.

Manual Quantization Selection

If you want explicit control over model quantization, use these commands:

• ollama run mistral:7b-instruct-q4_K_M → 4-bit (4GB, fastest, slight quality reduction)
• ollama run mistral:7b-instruct-q5_K_M → 5-bit (5GB, balanced)
• ollama run mistral:7b-instruct-q8_0 → 8-bit (7GB, highest quality, slower)

For most users with 8-12GB VRAM, q4_K_M provides the best experience. Only those with 16GB+ should consider q8_0 for daily use.

Chapter Six: VS Code Integration - The Real Power

Terminal-based chat is educational, but the true value emerges when your AI assistant integrates directly into your development environment, providing real-time suggestions, refactoring assistance, and instant documentation.

Installing the Continue Extension

Continue is an open-source VS Code extension specifically designed for local LLM integration. Unlike GitHub Copilot's closed ecosystem, Continue works with any OpenAI-compatible API—including Ollama.

1. Launch Visual Studio Code
2. Open the Extensions panel (click the building blocks icon in the left sidebar, or press Ctrl + Shift + X)
3. Search for "Continue" in the search bar
4. Locate the extension titled "Continue - Codestral, Claude, and more" by Continue
5. Click Install
6. After installation (~5 seconds), the Continue icon appears in the left activity bar

Connecting Continue to Ollama

Out of the box, Continue tries to use cloud APIs. We need to redirect it to your local Ollama instance:

1. Click the Continue icon in the sidebar to open the chat panel
2. At the bottom of the panel, click the model name dropdown (probably shows "GPT-4" or similar)
3. Select "Add Model" or click the gear icon for settings
4. Continue will open its config.json file in the editor
5. Locate the "models" array and add this configuration:

Alternatively, Continue offers an Autodetect feature that scans localhost:11434 and automatically adds available Ollama models. For most users, this is the simplest approach:

1. In Continue's settings, click "Autodetect Local Models"
2. Continue will find your Mistral model and add it to the available models list
3. Select "mistral" from the model dropdown

Your New Superpowers

With Continue connected to Ollama, you gain these capabilities:

1. Contextual Code Chat:

• Highlight a function or code block
• Press Ctrl + L (Windows/Linux) or Cmd + L (macOS)
• Ask: "What's the time complexity of this algorithm?" or "How can I optimize this?"
• The AI analyzes your selected code and provides context-aware responses
• Follow-up questions maintain conversation context

2. Inline Code Editing:

• Select code you want to modify
• Press Ctrl + I (or Cmd + I)
• Give instructions: "Convert this to async/await" or "Add error handling with try-catch"
• Continue generates the modified code inline as a diff
• Accept the change with Tab or reject with Esc

3. Autocomplete Suggestions:

• As you type code, Continue analyzes context and generates ghost text completions
• Suggestions appear in gray after a short delay (configurable, default ~300ms)
• Press Tab to accept, or keep typing to ignore
• Unlike basic autocomplete, these are multi-line, context-aware suggestions
• The model considers your entire file, imports, and recent changes

4. Command Palette Actions:

• Press Ctrl + Shift + P (or Cmd + Shift + P) to open the command palette
• Type "Continue" to see available actions:
• Continue: Explain Code - Get detailed explanations of selected code
• Continue: Generate Unit Tests - Create test cases automatically
• Continue: Generate Docstring - Add documentation to functions
• Continue: Fix Code - Attempt to debug errors in selected code

Chapter Seven: Advanced Configuration and Optimization

Now that your basic setup works, let's tune it for maximum performance and capability.

Expanding the Context Window

The context window determines how much conversation history and code the model can "remember" during a session. Mistral's default is 4,096 tokens (roughly 3,000 words or 200-300 lines of code).

For complex refactoring tasks requiring the model to understand multiple files, a larger context window is invaluable. Modern GPUs with 12GB+ VRAM can handle 16,384 or even 32,768 tokens.

To increase context window:

1. Create a file named Modelfile (no extension) in any directory
2. Add these contents:

FROM mistral
PARAMETER num_ctx 16384
PARAMETER temperature 0.7
PARAMETER repeat_penalty 1.1

3. In your terminal, navigate to the directory containing the Modelfile
4. Run: ollama create mistral-extended -f Modelfile
5. Wait 5-10 seconds while Ollama creates the custom model
6. In Continue's config, change the model name to "mistral-extended"

You've now created a customized model variant with 4x the memory. This allows you to paste entire classes or modules and ask comprehensive questions.

Crafting Effective System Prompts

The system prompt is a persistent instruction that shapes the model's behavior and output style. Think of it as setting your AI pair programmer's personality and expertise level.

For Clean Code Practices:

"You are a senior software engineer who prioritizes code readability, maintainability, and adherence to SOLID principles. Always include clear variable names, meaningful comments, and consider edge cases. When suggesting refactoring, explain the reasoning behind architectural choices."

For Speed and Conciseness:

"You are a code generator focused on efficiency. Provide only working code without lengthy explanations unless explicitly requested. Prioritize performance and minimize boilerplate. Use modern language features and idiomatic patterns."

For Learning and Education:

"You are a patient programming instructor. Break down complex concepts into simple explanations using analogies. Provide step-by-step implementations with inline comments explaining each section. Suggest additional learning resources when relevant."

For Security-Conscious Development:

"You are a security-aware senior developer. Always consider potential vulnerabilities like SQL injection, XSS, CSRF, and insecure deserialization. Suggest input validation, sanitization, and defensive programming patterns. Flag dangerous functions or libraries."

To set a system prompt in Continue, add this to your config.json:

"systemMessage": "Your chosen prompt here"

inside the model configuration object.

Tuning Generation Parameters

Several parameters control the randomness and style of generated code:

• temperature (0.0 to 2.0): Controls creativity vs. determinism. Lower = more predictable and conservative. Higher = more creative but potentially incorrect. Recommended for code: 0.2-0.5
• top_p (0.0 to 1.0): Alternative to temperature, controls diversity via nucleus sampling. Recommended: 0.9
• top_k (integer): Limits consideration to the top K most probable tokens. Recommended: 40-50
• repeat_penalty (1.0 to 2.0): Penalizes repetition. Too high causes incoherent output. Recommended: 1.05-1.15
• num_predict (integer): Maximum tokens to generate. Recommended: 512-1024 for code

Add these to your Modelfile:

PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER repeat_penalty 1.1
PARAMETER num_predict 1024

Chapter Eight: Beyond VS Code - Other IDE Integrations

Continue and Ollama aren't limited to VS Code. Here's how to use your local AI with other development environments.

JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, GoLand)

Continue offers a native plugin for JetBrains products:

1. Open your JetBrains IDE (IntelliJ IDEA, PyCharm, etc.)
2. Navigate to Settings → Plugins
3. Search for "Continue" in the Marketplace
4. Click Install and restart the IDE
5. Configure it identically to VS Code—point to http://localhost:11434

The functionality mirrors VS Code: inline suggestions, chat panel, and keyboard shortcuts (adapted to JetBrains conventions).

Cursor Editor

Cursor is an AI-first code editor built on VS Code's foundation. It has native AI features that can be redirected to Ollama:

1. Download Cursor from cursor.sh
2. Open Settings → Advanced → Model Settings
3. Change the API endpoint from OpenAI to http://localhost:11434/v1
4. Set the model name to "mistral" or your chosen Ollama model
5. Cursor's native features (Cmd+K, Cmd+L) now route to your local LLM

Neovim / Vim

For terminal-based editors, several plugins integrate with Ollama:

• gen.nvim: Lua-based plugin for Neovim with inline generation
• ollama.vim: Classic Vim plugin supporting Ollama's API
• codeium.vim: Can be configured to use local endpoints

Installation typically involves adding the plugin to your Neovim config (init.lua) and configuring the Ollama endpoint.

Direct API Usage

Ollama exposes a REST API compatible with OpenAI's format. You can integrate it into custom tools, scripts, or workflows:

Simple cURL example:

curl http://localhost:11434/api/generate -d '{
"model": "mistral",
"prompt": "Write a Rust function to parse JSON",
"stream": false
}'

Python example:

import requests

response = requests.post('http://localhost:11434/api/generate',
json={
"model": "mistral",
"prompt": "Explain quicksort algorithm",
"stream": False
})

print(response.json()['response'])

This flexibility lets you build custom integrations—CI/CD pipelines that generate documentation, pre-commit hooks that review code, or build scripts that suggest optimizations.

Chapter Nine: Comparing Local LLM Platforms

Ollama isn't the only game in town. Let's compare it against other popular local LLM platforms to help you make an informed choice.

Chapter Ten: Troubleshooting Common Issues

Even with straightforward installation, you might encounter challenges. Here are the most common issues and their solutions.

Issue #1: GPU Not Detected

Symptoms: Model runs on CPU with slow token generation (2-5 seconds per token instead of 20-80 tokens/second).

Solutions:

• Verify NVIDIA drivers are current: Download the latest from NVIDIA's official site. Driver version should be 535+ for best compatibility.
• Confirm CUDA Toolkit is installed: Open terminal and run nvidia-smi. You should see your GPU listed with driver version, temperature, and memory usage. If this command fails, CUDA isn't properly configured.
• Check GPU assignment: If you have multiple GPUs (integrated + discrete), ensure applications use the dedicated GPU. In Windows, go to Settings → Display → Graphics Settings and set Ollama to "High Performance".
• Set CUDA device explicitly: Create environment variable CUDA_VISIBLE_DEVICES=0 to force Ollama to use the first GPU (change number for multi-GPU systems).
• Restart Ollama service: After driver updates, close Ollama completely (check system tray) and restart it to reload GPU drivers.

Issue #2: Out of Memory Errors

Symptoms: Model loads initially but crashes with "CUDA out of memory" or "RuntimeError: CUDA error" after several generations.

Solutions:

• Switch to a smaller quantization: If using q8_0 (8-bit), downgrade to q4_K_M (4-bit) which uses half the VRAM.
• Reduce context window: If you increased num_ctx to 16384 or 32768, try 8192 or 4096. Larger contexts consume significantly more memory during generation.
• Close GPU-heavy applications: Background applications like games, 3D software, or browsers with hardware acceleration can fragment VRAM. Close everything except your IDE and Ollama.
• Use single model at a time: Running multiple models simultaneously multiplies VRAM usage. Stick to one active model.
• Enable system RAM offloading: In your Modelfile, add PARAMETER num_gpu 24 (adjust number based on your VRAM—use lower values to offload more to system RAM).
• Monitor VRAM usage: Use nvidia-smi -l 1 to watch real-time VRAM consumption and identify what's consuming memory.

Issue #3: Continue Extension Not Working

Symptoms: No autocomplete suggestions appear, chat panel doesn't respond, or requests time out.

Solutions:

• Verify Ollama is running: Look for the Ollama icon in your system tray. If absent, launch Ollama manually.
• Test API endpoint: Open a browser and navigate to http://localhost:11434. You should see "Ollama is running" message. If the page doesn't load, Ollama's API server didn't start correctly.
• Check Continue configuration: Open Continue's config.json and verify the model configuration has correct apiBase (http://localhost:11434) and model name matches what's in ollama list.
• Restart VS Code: Sometimes extensions need a fresh start. Use Ctrl + Shift + P → "Developer: Reload Window".
• Check firewall settings: Windows Firewall or third-party security software might block localhost connections. Add an exception for Ollama and VS Code.
• Review extension logs: Open VS Code's Output panel (View → Output), select "Continue" from the dropdown, and look for error messages indicating what's failing.
• Reinstall Continue: Uninstall the extension, restart VS Code, and reinstall. Sometimes cached configuration causes persistent issues.

Issue #4: Poor Code Quality

Symptoms: Generated code has bugs, doesn't match requirements, or is syntactically incorrect.

Solutions:

• Upgrade to a larger model: 7B models have limitations. Try ollama pull mixtral:8x7b (requires 24GB+ VRAM) or llama3:13b (requires 16GB+ VRAM) for dramatically better reasoning.
• Improve prompt specificity: Instead of "write a sort function", try "write a stable merge sort function for an array of integers in Python 3.11 with type hints and docstrings, handling edge cases like empty arrays".
• Optimize system prompt: Explicitly request test coverage, error handling, and documentation in your system prompt.
• Lower temperature: Reduce from default 0.7-0.8 to 0.2-0.3 for more deterministic, conservative code generation.
• Provide more context: Include relevant imports, class definitions, or surrounding code so the model understands your codebase's patterns and conventions.
• Iterate and refine: Use the generated code as a starting point. Ask follow-up questions like "add error handling" or "optimize for performance".
• Try specialized models: For specific languages, use domain-specific models like codellama:python or deepseek-coder.

Issue #5: Download Interruptions

Symptoms: ollama pull starts downloading but stops partway, shows network errors, or appears frozen.

Solutions:

• Ensure sufficient disk space: Models require 4-20GB depending on size. Check you have at least 50GB free on the installation drive.
• Verify internet stability: Ollama downloads large files (4GB+). Unstable connections cause failures. Consider downloading during off-peak hours or using wired ethernet instead of Wi-Fi.
• Resume interrupted downloads: Ollama automatically resumes from the last checkpoint. Simply run the same ollama pull command again.
• Change VPN server: If using a VPN, some servers have bandwidth restrictions. Try different servers or temporarily disable VPN during download.
• Clear partial downloads: If a model is corrupted, run ollama list to see partial downloads, then ollama rm [model_name] to delete and retry from scratch.
• Use a download manager: For extremely slow or unstable connections, download the model manually from Ollama's model library, then import it locally.

Issue #6: Slow Performance Despite Good Hardware

Symptoms: RTX 3080/4080 generates only 10-15 tokens/second when it should achieve 40-60+.

Solutions:

• Check thermal throttling: Run nvidia-smi dmon to monitor GPU temperature and clock speeds. If temp exceeds 80°C, GPU may be throttling. Improve case airflow or adjust fan curves.
• Verify PCIe link speed: Run nvidia-smi -q | findstr "Width" (Windows) or nvidia-smi -q | grep "Width" (Linux/Mac). Should show "x16" for full bandwidth. If showing x8 or x4, reseat the GPU or use a different PCIe slot.
• Disable power saving: In NVIDIA Control Panel, set "Power Management Mode" to "Prefer Maximum Performance" instead of "Adaptive".
• Update Ollama: Run ollama --version and compare to the latest version on Ollama's website. Updates often include performance optimizations.
• Profile with better quantization: Sometimes INT4 is actually slower than INT8 due to de-quantization overhead on certain GPUs. Test different quantizations (q4_K_M vs q5_K_M vs q8_0) to find the fastest for your hardware.

Chapter Eleven: Real-World Performance Metrics

Let's establish realistic expectations with actual benchmarks from various hardware configurations.