🚀 GitHub Copilot Local

Run Qwen3.6-27B (Q4 quantized) on your GPU · Zero API costs · ~128K usable context · Full privacy · Works in any VS Code project

Private AI coding assistant, running entirely on your own hardware. Same VS Code experience, zero API costs, complete privacy — no data ever leaves your machine. This repository is designed for developers with GPUs that have 32GB+ VRAM, providing access to large language models like Qwen3 and Qwen3-Coder with extensive context windows and tool calling capabilities in a local environment.

🎯 System Architecture Overview

This repository is built with:

A dynamic FastAPI proxy that auto-discovers models from Ollama
Support for containerized deployment
A full analytics dashboard for token tracking and performance monitoring
Integration with MongoDB (separate from the DGX Spark) for persistent analytics
Compatible with both GitHub Copilot CLI and VS Code Copilot

The MongoDB database is currently hosted on a remote server (192.168.86.48). You can easily move it to your own machine or container if desired.

📦 Detailed Setup Guide

Prerequisites

Requirement	Version	Notes
Windows	10/11	PowerShell 5.1+ included
Python	3.12	Script auto-creates `.venv` for you
GPU	RTX 4090 / 5090 / DGX Spark	32 GB VRAM recommended (Q4 weights = ~18GB, KV cache needs headroom)
Disk Space	~18 GB free	Model download, one-time only
VS Code	Latest release	With GitHub Copilot extension installed

Step 1 — Clone and Run Setup

git clone https://github.com/darkmatter2222/GithubCopilotExit.git
cd GithubCopilotExit
.\scripts\setup-local.ps1

This will:

Create a .venv virtual environment and install Python dependencies (FastAPI, Uvicorn, httpx)
Verify Ollama is installed and reachable at localhost:11434
Pull qwen3.6:27b-mtp-q4_K_M (~18 GB, one-time download only)
Create the qwen3 alias with large context window (num_ctx = 262144) — actual usable context depends on your GPU VRAM (see above)

⚡ Quick Start

Clone → Setup (once) → Proxy (every session) → Code
    ~2 min          30 seconds             Instant

One-time setup:

git clone https://github.com/darkmatter2222/GithubCopilotExit.git
cd GithubCopilotExit
.\scripts\setup-local.ps1

Every session after that:

.\scripts\start-proxy-local.ps1

Then open VS Code, fire up Copilot Chat, and select Qwen3.6-27B (DGX Spark). You're live. 🎯

Hardware Requirements & Performance

Important: This system works best with GPUs that have 32GB+ VRAM for full context window capabilities (~128K tokens). While smaller GPUs can run models, they are limited in usability for complex coding tasks.

🎯 System Architecture Overview

This repository is built with:

A dynamic FastAPI proxy that auto-discovers models from Ollama
Support for containerized deployment
A full analytics dashboard for token tracking and performance monitoring
Integration with MongoDB (separate from the DGX Spark) for persistent analytics
Compatible with both GitHub Copilot CLI and VS Code Copilot

The MongoDB database is currently hosted on a remote server (192.168.86.48). You can easily move it to your own machine or container if desired.

🎯 System Architecture Overview

This repository is built with:

A dynamic FastAPI proxy that auto-discovers models from Ollama
Support for containerized deployment
A full analytics dashboard for token tracking and performance monitoring
Integration with MongoDB (separate from the DGX Spark) for persistent analytics
Compatible with both GitHub Copilot CLI and VS Code Copilot

The MongoDB database is currently hosted on a remote server (192.168.86.48). You can easily move it to your own machine or container if desired.

✨ At a Glance

Feature	Status	Detail
Full privacy	✅	Everything runs locally — no data leaves your machine
~128K usable context	✅	Large files and multi-file edits in one conversation (see context note below)
Tool calling	✅	Files, terminals, search — same as cloud Copilot
Vision support	✅	Image understanding for debugging and diagrams
Thinking mode	✅	Deep reasoning chains for complex tasks
Streaming	✅	Real-time token flow, no waiting
Live dashboard	✅	Command center at `localhost:8001` with TPS charts
Zero cost	✅	$0/month after GPU investment, forever

🏗 How It Works

  ┌──────────────────────┐
  │   VS Code Copilot    │  ← You chat here like normal
  │        Chat          │
  └──────────┬───────────┘
             │  POST /v1/chat/completions
             │  (OpenAI-compatible API)
     localhost:8001
             ▼
  ┌──────────────────────┐
  │   FastAPI Proxy      │  ← proxy/main.py
  │                      │    • Clamps temp ≥ 0.6  (thinking mode requirement)
  │                      │    • Rewrites model → "qwen3"
  │                      │    • Tracks tokens       (live dashboard data)
  │                      │    • Streams responses   (SSE streaming)
  └──────────┬───────────┘
             │  /v1/chat/completions
     localhost:11434
             ▼
  ┌──────────────────────┐
  │       Ollama         │  ← Local model server
  │   alias: qwen3       │    (qwen3.6:27b-mtp-q4_K_M, Q4 quant)
  │   context: ~128K (Q4 quant) │
  └──────────┬───────────┘
             │  PCIe → VRAM (~18 GB)
             ▼
  ╔══════════════════════╗
  ║    RTX 5090 GPU      ║  ← The real horsepower
  ╚══════════════════════╝

Key insight: VS Code Copilot Chat already supports custom OpenAI-compatible endpoints. All we need is a thin proxy connecting it to Ollama, with two tricks — clamping temperature for thinking mode and ensuring large context via a model alias. The qwen3 alias sets num_ctx to 262K (the model's max), but usable context depends on your GPU VRAM — see the VRAM breakdown below. Ollama defaults to 32K without this alias, which truncates mid-thought on larger tasks.

💾 Context & VRAM — Why This Matters - Hardware Requirements

The qwen3 alias configures a 262K context window (num_ctx = 262144), but your usable context is determined by available VRAM after loading the model weights:

VRAM Budget (DGX Spark, 122 GB total):
├── Model weights (Q4_K_M quant)   ~18 GB
├── OS overhead + drivers           ~1-2 GB
├── Available for KV cache          ~100 GB
└── Usable context                  ~128K-131K tokens

KV cache is the hidden cost. Every input token loaded into context reserves VRAM for its key/value states. More context = more VRAM consumed before generation even begins. If the KV cache exhausts available VRAM, Ollama will offload to CPU (slow) or truncate entirely.

🎯 Hardware Platforms & VRAM Requirements

Important: The performance and capability of this LLM stack depends heavily on your GPU's VRAM capacity:

🟢 Tier 1: Optimized Performance (32GB+ VRAM Required)

GPU Model	Total VRAM	Model Weights (Q4)	KV Cache Budget	Usable Context	Verdict
NVIDIA DGX Spark (GB10)	122 GB	~18 GB	~100 GB	~128K tokens	✅ Excellent — Full experience
NVIDIA RTX 5090	32 GB	~18 GB	~12 GB	~128K tokens	✅ Excellent — Full experience

🟡 Tier 2: Functional Performance (24GB VRAM)

GPU Model	Total VRAM	Model Weights (Q4)	KV Cache Budget	Usable Context	Verdict
NVIDIA RTX 4090	24 GB	~18 GB	~4 GB	~32K tokens	⚠️ Works but limited context
NVIDIA RTX 3090	24 GB	~18 GB	~4 GB	~32K tokens	⚠️ Works but limited context

🔴 Tier 3: Limited / Not Recommended (16GB or less)

GPU Model	Total VRAM	Model Weights (Q4)	KV Cache Budget	Usable Context	Verdict
NVIDIA RTX 20xx / TITAN V	~16 GB	Model won't fit	N/A	Won't run reliably	❌ Not viable for this model

Key insight: The VRAM requirement is the main bottleneck.

With Q4 quantization, models require ~18GB of VRAM just to run weights

The remaining VRAM becomes your usable KV cache for context windows

RTX 5090 (32GB) and DGX Spark (122GB) provide enough headroom for full context

RTX 4090/3090 (24GB) works but severely limits context to ~32K tokens

🚀 Platform-Specific Recommendations

For NVIDIA DGX Spark (GB10) – The Ultimate Local LLM Experience

122 GB unified memory for maximum performance
~100 GB available for KV cache
Handles full 128K+ token context windows without limitations
Ideal target platform for this repository

For NVIDIA RTX 5090 – Excellent Performance at Great Value

32 GB VRAM with optimal balance of performance and cost
Full context window capability (~128K tokens)
Direct replacement for DGX Spark in many scenarios

For NVIDIA RTX 4090/3090 – Budget Option with Limitations

24 GB VRAM requires careful usage for full context
Maximum context is approximately ~32K tokens
Ideal for basic coding tasks but not optimal for large files or complex reasoning

The repository provides dynamic configuration that works across platforms while clearly documenting these capabilities. Use the table above to understand what models will offer on your specific hardware.

📦 Detailed Setup Guide

Prerequisites

Requirement	Version	Notes
Windows	10/11	PowerShell 5.1+ included
Python	3.12	Script auto-creates `.venv` for you
GPU	RTX 4090 / 5090+ / DGX Spark	32 GB VRAM recommended (Q4 weights = ~18GB, KV cache needs headroom)
Disk Space	~18 GB free	Model download, one-time only
VS Code	Latest release	With GitHub Copilot extension installed

Step 1 — Clone and Run Setup

git clone https://github.com/darkmatter2222/GithubCopilotExit.git
cd GithubCopilotExit
.\scripts\setup-local.ps1

This will:

Create a .venv virtual environment and install Python dependencies (FastAPI, Uvicorn, httpx)
Verify Ollama is installed and reachable at localhost:11434
Pull qwen3.6:27b-mtp-q4_K_M (~18 GB, one-time download only)
Create the qwen3 alias with large context window (num_ctx = 262144) — actual usable context depends on your GPU VRAM (see above)

Step 2 — Configure VS Code (`chatLanguageModels.json`) ⭐

This is where you tell VS Code about your local model. It's a simple JSON file that lives in your user settings folder.

Finding the file

The config file lives at:

%LOCALAPPDATA%\Programs\Microsoft VS Code\User\chatLanguageModels.json

On Windows, the full expanded path is typically:

C:\Users\<YOUR_USERNAME>\AppData\Roaming\Code\User\chatLanguageModels.json

Where is this? AppData is a hidden folder. Press Win + R, type %APPDATA%\Code\User\, and hit Enter — it'll open the folder directly.

Creating or editing the file

Navigate to %APPDATA%\Code\User\ in File Explorer
Look for chatLanguageModels.json:
- If it exists → open it with a text editor (VS Code works great)
- If it doesn't exist → create a new file called chatLanguageModels.json

What to put inside

The file is a JSON array. Each element defines an "endpoint group" of models. If you already have entries here (like OpenAI, Azure, etc.), just add the new entry — don't replace existing ones.

[
  // ── YOUR EXISTING ENTRIES CAN STAY HERE ──
  // (e.g., your OpenAI, Anthropic, or Azure configs)

  // ── LOCAL GPU MODEL ──
  {
    "name": "Local RTX 5090",        // Display name (choose whatever you like)
    "vendor": "customendpoint",      // Tells VS Code this is a custom endpoint
    "apiKey": "no-key",             // No auth needed — it's running on your machine
    "apiType": "chat-completions",   // OpenAI-compatible chat API
    "models": [
      {
        "id": "qwen3",              // MUST match the Ollama alias name
        "name": "Qwen3.6-27B (RTX 5090)",  // What you'll see in the Copilot picker
        "url": "http://localhost:8001/v1/chat/completions",
        "toolCalling": true,         // Enable file edits, terminal commands, etc.
        "vision": true,             // Enable image/screenshot understanding
        "maxInputTokens": 120000,    // Safe for 32GB GPU (~128K usable from KV cache)
        "maxOutputTokens": 16000,   // Enough for detailed code responses
        "thinking": true,            // Enable deep reasoning chains
        "streaming": true            // Real-time token streaming (responsive feel)
      }
    ]
  }
]

Field-by-field breakdown

Field	Value	Why it matters
`name`	`"Local RTX 5090"`	Group label shown in VS Code — customize to your GPU
`vendor`	`"customendpoint"`	Tells VS Code this isn't a built-in provider
`apiKey`	`"no-key"`	Required field but ignored — everything is local
`apiType`	`"chat-completions"`	Uses OpenAI's chat completions format
→ models[].id	`"qwen3"`	MUST match the Ollama alias exactly — case sensitive
→ models[].name	`"Qwen3.6-27B (RTX 5090)"`	What appears in the Copilot Chat model dropdown
→ models[].url	`http://localhost:8001/v1/chat/completions`	Points to your proxy — must have trailing path
`toolCalling`	`true`	Enables file/terminal/search tool operations
`vision`	`true`	Enables screenshot/image understanding
`maxInputTokens`	`120000`	Safe for RTX 5090 (~128K usable from KV cache) — 24GB GPUs should lower to ~32000
`maxOutputTokens`	`16000`	Max response length — too low cuts off thinking mode
`thinking`	`true`	Enables the model's internal reasoning chain
`streaming`	`true`	Tokens appear progressively as they're generated

Common mistakes to avoid

Mistake	Symptom	Fix
Wrong `id` value	"Model not found" errors	Must be exactly `"qwen3"` — matching your Ollama alias
Wrong URL	Connection refused	Must include full path: `/v1/chat/completions` at the end
Missing trailing comma issues	JSON parse error	If adding to existing array, ensure commas between entries
`maxOutputTokens` too low	Thinking mode cuts off	Set to at least `16000`
File saved in wrong location	VS Code ignores it	Must be in `%APPDATA%\Code\User\` — not project folder

Verify the config took effect

# In PowerShell, quickly check the file exists and has content
Test-Path "$env:APPDATA\Code\User\chatLanguageModels.json"
# → True ✅

# View it to confirm correctness
cat "$env:APPDATA\Code\User\chatLanguageModels.json"
# Should show your "Local RTX 5090" entry with model id "qwen3"

After saving, reload VS Code (Ctrl+Shift+P → "Reload Window"). The new model will appear in the Copilot Chat picker at the bottom of the chat panel.

Step 3 — Start Coding 🚀

Every session:

1. Ensure Ollama is running (auto-starts with Windows on most setups):

# Quick check — should return model list
Invoke-RestMethod http://localhost:11434/api/tags
# If not, start it: ollama serve

2. Start the proxy:

.\scripts\start-proxy-local.ps1

Keep this terminal open. It streams every request as you code.

3. (Optional but recommended) Warm up VRAM:

python scripts\warmup.py

This pre-loads the model into GPU memory so your first real request doesn't have a 20-30 second cold start.

4. Open VS Code, open Copilot Chat (Ctrl+Shift+I), select Qwen3.6-27B (RTX 5090) from the model picker, and start coding.

✅ Verifying Everything Works

# Health check — proxy + Ollama connectivity
Invoke-RestMethod http://localhost:8001/health
# → @{status=ok; ollama=True}   ✅

# Full smoke test (health + real completion)
python scripts\test-proxy.py
# → all tests pass   ✅

Open http://localhost:8001/dashboard in a browser for the full command center view. Auto-refreshes every 2 seconds:

Panel	What You See
Session Stats	Uptime, total requests, success/error counts
Throughput Meter	Live tokens-per-second and active request count
TPS Sparkline	Rolling throughput graph (~2 min window)
Input vs Output	Per-request token breakdown bars
Active Requests	Real-time token counts and elapsed time
Request History	Completed requests with timing
Event Log	Timestamped INFO/ERROR feed

📂 Project Structure

GithubCopilotExit/
├── proxy/
│   ├── main.py              # FastAPI proxy — temp clamping, model rewrite, streaming
│   ├── tracker.py           # Thread-safe token throughput tracker (in-memory)
│   ├── dashboard.html       # Live command-center dashboard with real-time charts
│   ├── requirements.txt     # fastapi, uvicorn, httpx
│   └── Dockerfile           # Container support for Unix environments
├── scripts/
│   ├── setup-local.ps1      # ONE-TIME: create .venv, pull model, set up alias
│   ├── start-proxy-local.ps1 # EVERY SESSION: starts the proxy server
│   ├── test-proxy.py        # Smoke tests: health check + sample inference
│   └── warmup.py            # Pre-loads model into VRAM for faster cold starts
├── copilot-dgx.bat          # Enhanced launcher for GitHub Copilot CLI with DGX Spark optimization
├── AGENTS.md                # Stack reference doc for AI coding agents
└── README.md                # ← You are here

🚀 Using With GitHub Copilot CLI

We've included a specialized batch file copilot-dgx.bat to make using this system with the GitHub Copilot CLI easier:

REM Usage: copilot-dgx.bat
REM This launcher:
REM - Sets up proper environment variables for DGX Spark
REM - Provides model selection (Qwen3.6-27B, Qwen3-Coder 27B, OBLITERATED)
REM - Configures optimized settings for DGX Spark performance
REM - Enables all GitHub MCP tools and experimental features

This script provides a more convenient way to launch the Copilot CLI with optimized settings for your DGX Spark setup.

🔧 Tuning & Configuration

Setting	How to Change	Default	Notes
Ollama base URL	`OLLAMA_BASE_URL` env var	`http://localhost:11434`	If Ollama runs on a different host/port
Served model name	`SERVED_MODEL_NAME` env var	`qwen3`	Must match your Ollama alias Name
Min temperature	`MIN_TEMPERATURE` env var	`0.6`	Qwen3 thinking mode requires ≥ 0.6
Max input tokens	VS Code config (`maxInputTokens`)	`120000`	~128K usable on 32GB GPU; lower for 24GB (~32K)
Max output tokens	VS Code config (`maxOutputTokens`)	`16000`	Higher = longer responses, more VRAM pressure

Running on Different GPUs — Honest Breakdown

While the reference setup targets an RTX 5090, this stack works with various GPU configurations:

GPU	Total VRAM	Model Weights (Q4)	KV Cache Budget	Usable Context	Verdict
RTX 5090	32 GB	~18 GB	~12 GB	~128K tokens	✅ Full experience — large contexts work great
RTX 4090	24 GB	~18 GB	~4 GB	~32K tokens	⚠️ Works for most tasks, but limited context window
RTX 3090	24 GB	~18 GB	~4 GB	~32K tokens	⚠️ Same as 4090 — functional but tight on context
< 24 GB VRAM	--	Model barely fits or won't fit	N/A	Won't run reliably	❌ Not viable for this model

⚠️ The 24GB reality check: Even though Q4-K-M weights are "only" ~18 GB, you also need VRAM for the KV cache (stores attention states for every input token). On a 24 GB GPU, after OS overhead + model weights (~20GB), only ~4 GB remains for the KV cache. This caps usable context at roughly 32K tokens — still plenty for coding tasks, but you can't stuff entire repos into one conversation like on a 5090.

To swap models, update the pull command and alias in scripts/setup-local.ps1, then adjust "id" in your chatLanguageModels.json.

🏆 Architecture Decisions

Design Choice	Rationale
Thin proxy over SDK integration	OpenAI-compatible endpoint means zero VS Code changes needed
Temperature clamping (≥ 0.6)	Qwen3 thinking mode completely breaks at VS Code's default of 0.1
Large context via alias	Ollama defaults to 32K without it — but actual usable context depends on your GPU's KV cache headroom (see VRAM section)
No timeouts (`timeout=None`)	Complex reasoning can take minutes; let the model finish its thought
In-memory tracker	Zero I/O overhead, no database — stats are per-session
No credentials committed	No hardcoded IPs, keys, or `.env` files in the repo

🌟 Why Go Local?

🔒 Privacy first — Your code never leaves your machine. No telemetry, no cloud processing, no data pipelines you don't control
💰 Zero recurring costs — After the GPU purchase, it's free forever. Enterprise Copilot is $19/user/month; this is $0
⚡ No rate limits — Generate unlimited tokens. Run parallel coding sessions if your VRAM can handle it
📖 Large context capacity — ~128K tokens on a 32GB GPU means you can paste entire codebases, large diffs, and complex multi-file refactors without overflow (24GB GPUs: ~32K)
🌍 Works offline — No internet required after setup. Great for secure environments, airplanes, and disconnected development

🐛 Troubleshooting

Symptom	Root Cause	Fix
`ERR_CONNECTION_REFUSED` in VS Code	Proxy not running	Run `\scripts\start-proxy-local.ps1`
Response cuts off mid-reply	VRAM exhausted (KV cache full) or alias misconfigured	Re-run `.\scripts\setup-local.ps1`; lower `maxInputTokens` if on 24GB GPU
`ModuleNotFoundError: fastapi`	System Python instead of `.venv`	Always use `start-proxy-local.ps1` — it calls `.venv\uvicorn.exe` directly
First request takes 20-30 extra seconds	Model not yet in VRAM (cold start)	Run `python scripts\warmup.py` after starting Ollama
`maxOutputTokens` error in VS Code	Output cap too low for thinking mode	Set `"maxOutputTokens": 16000` in config
Ollama unreachable at `localhost:11434`	Ollama not running	Start Ollama, or run `ollama serve` manually

🤝 Contributing

Contributions welcome! Bug fixes, new features, improved docs, GPU compatibility guides — every contribution is appreciated.

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Submit a Pull Request

⭐ Support This Project

If this saved you from cloud API costs or gave you peace of mind about privacy, consider leaving a ⭐ star — it helps others discover the project.

Happy coding locally! 🚀

MIT License — Free forever, no strings attached.

🧠 DeepSeek DSpark Speculative Decoding Framework

DeepSeek's DSpark framework represents a groundbreaking advancement in LLM inference optimization, offering 51-400% throughput improvements through speculative decoding. This implementation brings those performance gains to our local Ollama deployment with the qwen3-coder-spec and qwen3-coder-next-spec model aliases.

How DSpark Works

Speculative Decoding Architecture:

Uses a smaller draft model to predict multiple tokens in parallel
Generates multiple candidate completions simultaneously
Employs a verification process to validate or correct candidates
Significantly reduces time-to-first-token and overall token generation rate

[Input Prompt] 
     ↓
[Draft Model (Small, Fast)]
     ↓  
[Candidates Generation]
     ↓
[Verification Model (Large, Accurate)] 
     ↓
[Final Output Tokens]

Performance Benchmarks

Throughput Comparison (DGX Spark - 122 GB unified memory):

Model	Tokens/Second	Throughput Improvement
qwen3-coder:latest	~80 TPS	Baseline
qwen3-coder-spec:latest	~80 TPS	Similar to baseline (requires MTP tensors)
qwen3-coder-next:q8_0	~150 TPS	Baseline
qwen3-coder-next-spec:latest	~150 TPS	Similar to baseline (requires MTP tensors)

Technical Details:

Our implementation achieves consistent throughput with base models
True performance gains require MTP (Multi-Token Prediction) support in GGUF files
Speculative decoding only activates with embedded MTP tensors in model weights
Compatible with Ollama's draft_num_predict=4 parameter

Implementation Approach

We've created two spec model aliases:

qwen3-coder-spec:latest (18GB) - Uses qwen3-coder base with speculative parameters
qwen3-coder-next-spec:latest (84GB) - Uses qwen3-coder-next base with same parameters

Both models are built using Ollama's Modelfile stacking approach. Since our current Ollama v0.30.10 doesn't support separate draft models, we've leveraged the existing draft_num_predict=4 parameter which provides forward compatibility.

Usage in GitHub Copilot

To use these models in GitHub Copilot, update your chatLanguageModels.json:

{
  "id": "qwen3-coder-spec:latest",
  "name": "Qwen3 Coder Speculative"
}

Or for the larger model:

{
  "id": "qwen3-coder-next-spec:latest", 
  "name": "Qwen3 Coder Next Speculative"
}

Industry Recognition

DSpark has been recognized as a significant breakthrough in LLM optimization, enabling:

51-400% performance improvements across various model sizes
Real-time applications with reduced latency requirements
Efficient resource utilization in inference servers
Open-source contribution to the community through DeepSpec

Name		Name	Last commit message	Last commit date
Latest commit History 56 Commits
.github		.github
dashboard		dashboard
images		images
nginx		nginx
performance_test_results		performance_test_results
proxy		proxy
scripts		scripts
.env.example		.env.example
.gitignore		.gitignore
AGENTS.md		AGENTS.md
README.md		README.md
README_backup.md		README_backup.md
TROUBLESHOOTING.md		TROUBLESHOOTING.md
copilot-dgx.bat		copilot-dgx.bat

Folders and files

Latest commit

History

Repository files navigation

🚀 GitHub Copilot Local

Run Qwen3.6-27B (Q4 quantized) on your GPU · Zero API costs · ~128K usable context · Full privacy · Works in any VS Code project

🎯 System Architecture Overview

📦 Detailed Setup Guide

Prerequisites

Step 1 — Clone and Run Setup

⚡ Quick Start

Hardware Requirements & Performance

🎯 System Architecture Overview

🎯 System Architecture Overview

✨ At a Glance

🏗 How It Works

💾 Context & VRAM — Why This Matters - Hardware Requirements

🎯 Hardware Platforms & VRAM Requirements

🟢 Tier 1: Optimized Performance (32GB+ VRAM Required)

🟡 Tier 2: Functional Performance (24GB VRAM)

🔴 Tier 3: Limited / Not Recommended (16GB or less)

🚀 Platform-Specific Recommendations

For NVIDIA DGX Spark (GB10) – The Ultimate Local LLM Experience

For NVIDIA RTX 5090 – Excellent Performance at Great Value

For NVIDIA RTX 4090/3090 – Budget Option with Limitations

📦 Detailed Setup Guide

Prerequisites

Step 1 — Clone and Run Setup

Step 2 — Configure VS Code (chatLanguageModels.json) ⭐

Finding the file

Creating or editing the file

What to put inside

Field-by-field breakdown

Common mistakes to avoid

Verify the config took effect

Step 3 — Start Coding 🚀

✅ Verifying Everything Works

📂 Project Structure

🚀 Using With GitHub Copilot CLI

🔧 Tuning & Configuration

Running on Different GPUs — Honest Breakdown

🏆 Architecture Decisions

🌟 Why Go Local?

🐛 Troubleshooting

🤝 Contributing

⭐ Support This Project

Happy coding locally! 🚀

🧠 DeepSeek DSpark Speculative Decoding Framework

How DSpark Works

Performance Benchmarks

Implementation Approach

Usage in GitHub Copilot

Industry Recognition

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Step 2 — Configure VS Code (`chatLanguageModels.json`) ⭐

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