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Gemma 3-1B Tutorial - Google Colab

Complete tutorial for running Gemma 3-1B with llcuda v2.1.0 on Tesla T4 GPU.

🚀 Open in Google Colab

📚 What This Tutorial Covers

This comprehensive 14-step tutorial demonstrates:

  1. GPU Verification - Detect Tesla T4 and check compatibility
  2. Installation - Install llcuda v2.1.0 from GitHub
  3. Binary Download - Auto-download CUDA binaries (~266 MB)
  4. GPU Compatibility - Verify llcuda can use the GPU
  5. Model Loading - Load Gemma 3-1B-IT from Unsloth HuggingFace
  6. First Inference - Run general knowledge queries
  7. Code Generation - Test Python code generation
  8. Batch Inference - Process multiple prompts efficiently
  9. Performance Metrics - Analyze throughput and latency
  10. Advanced Parameters - Explore generation strategies
  11. Model Loading Methods - HuggingFace, Registry, Local paths
  12. Unsloth Workflow - Fine-tuning to deployment pipeline
  13. Context Manager - Auto-cleanup resources
  14. Available Models - Browse Unsloth GGUF models

📈 Verified Performance

Real execution results from Google Colab Tesla T4:

  • Speed: 134 tokens/sec average (range: 116-142 tok/s)
  • Latency: 690ms median
  • Consistency: Stable performance across all tests
  • GPU Offload: 99 layers fully on GPU

3x Faster Than Expected!

Initial estimates: ~45 tok/s Actual performance: 134 tok/s FlashAttention + Tensor Cores delivering exceptional results!

📝 Tutorial Steps

Step 1: Verify Tesla T4 GPU

!nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv

# Expected output:
# Tesla T4, 7.5, 15360 MiB

Step 2: Install llcuda v2.1.0

!pip install -q git+https://github.com/waqasm86/llcuda.git

# ✅ llcuda v2.1.0 installed successfully!

Step 3: Import and Download Binaries

import llcuda

# First import triggers binary download:
# - Source: GitHub Releases v2.0.6
# - Size: 266 MB
# - Duration: ~1-2 minutes
# - Cached for future use

Download Output: 

📥 Downloading from GitHub releases...
URL: https://github.com/waqasm86/llcuda/releases/download/v2.0.6/...
Downloading T4 binaries: 100% (266.0/266.0 MB)
✅ Extraction complete!
Copied 5 binaries to .../llcuda/binaries/cuda12
Copied 18 libraries to .../llcuda/lib

Step 4: Load Gemma 3-1B-IT

engine = llcuda.InferenceEngine()

engine.load_model(
    "unsloth/gemma-3-1b-it-GGUF:gemma-3-1b-it-Q4_K_M.gguf",
    silent=True
)

# Auto-configured for Tesla T4:
# - GPU Layers: 99 (full offload)
# - Context Size: 4096 tokens
# - Batch Size: 2048

Step 5: Run Inference

result = engine.infer(
    "Explain quantum computing in simple terms",
    max_tokens=200,
    temperature=0.7
)

print(result.text)
print(f"Speed: {result.tokens_per_sec:.1f} tok/s")

# Actual output: 131.4 tokens/sec ✅

Step 6: Batch Processing

prompts = [
    "What is machine learning?",
    "Explain neural networks briefly.",
    "What is the difference between AI and ML?",
    "Define deep learning concisely."
]

results = engine.batch_infer(prompts, max_tokens=80)

for prompt, result in zip(prompts, results):
    print(f"Q: {prompt}")
    print(f"A: {result.text}")
    print(f"Speed: {result.tokens_per_sec:.1f} tok/s\n")

# Results:
# Query 1: 116.0 tok/s
# Query 2: 142.3 tok/s
# Query 3: 141.6 tok/s
# Query 4: 141.7 tok/s

📊 Performance Results

From the executed notebook:

Test Tokens Speed Latency
General Knowledge 200 131.4 tok/s 1522ms
Code Generation 300 136.1 tok/s -
Batch Query 1 80 116.0 tok/s 690ms
Batch Query 2 80 142.3 tok/s 562ms
Batch Query 3 80 141.6 tok/s 565ms
Batch Query 4 80 141.7 tok/s 565ms
Average - 134.2 tok/s 690ms median

Why So Fast?

  1. FlashAttention - 2-3x speedup for attention operations
  2. Tensor Cores - SM 7.5 fully utilized
  3. CUDA Graphs - Reduced kernel launch overhead
  4. Full GPU Offload - All 99 layers on GPU
  5. Q4_K_M Quantization - Optimal speed/quality balance

📦 Model Information

Gemma 3-1B-IT Q4_K_M:

  • Size: ~806 MB (download)
  • Parameters: 1 billion
  • Quantization: Q4_K_M (4-bit)
  • Context: 2048 tokens (expandable to 4096)
  • VRAM: ~1.2 GB
  • Source: unsloth/gemma-3-1b-it-GGUF

📓 Jupyter Notebook Features

The notebook includes:

✅ Complete Setup Guide - Step-by-step installation ✅ GPU Verification - Ensure you have Tesla T4 ✅ Error Handling - Helpful troubleshooting tips ✅ Multiple Examples - Chat, batch, creative generation ✅ Performance Metrics - Detailed throughput & latency ✅ Unsloth Workflow - Fine-tuning to deployment ✅ Model Catalog - List of available Unsloth models

❓ Common Questions

How long does the first run take?

  • Binary download: 1-2 minutes (266 MB)
  • Model download: 2-3 minutes (~800 MB)
  • Model loading: 10-20 seconds
  • First inference: Same speed as subsequent runs

Total first-time setup: ~5 minutes Subsequent sessions: Instant (cached binaries and models)

Can I use different models?

Yes! The notebook works with any GGUF model from HuggingFace:

# Llama 3.2-3B
engine.load_model(
    "unsloth/Llama-3.2-3B-Instruct-GGUF:Llama-3.2-3B-Instruct-Q4_K_M.gguf"
)

# Qwen 2.5-7B
engine.load_model(
    "unsloth/Qwen2.5-7B-Instruct-GGUF:Qwen2.5-7B-Instruct-Q4_K_M.gguf"
)

What if I don't have T4?

llcuda v2.1.0 is optimized for Tesla T4. Other GPUs may work but performance will vary. The binaries are compiled for SM 7.5 (T4's compute capability).

🚀 Get Started Now!

No GPU? No problem! Google Colab provides free Tesla T4 access.

Questions? Open an issue on GitHub