5 min read
Open Source AI Progress: The Democratization of Foundation Models
Open source AI made remarkable progress in 2024. Let’s examine the landscape and what it means for enterprise adoption.
The Open Source AI Landscape
Major Models Released in 2024
Model Family Size Range Notable Features
──────────────────────────────────────────────────────
Llama 3.1 8B-405B Matches GPT-4 at 405B
Mistral/Mixtral 7B-8x22B MoE efficiency
Phi-3 3.8B-14B Efficiency champion
Qwen 2 0.5B-72B Strong multilingual
Command R+ 104B RAG-optimized
Falcon 2 11B-180B Multilingual focus
Gemma 2 2B-27B Google's open offeringLlama 3.1: The Game Changer
# Llama 3.1 405B approaches GPT-4 quality
# And it's fully open source
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load Llama 3.1 70B (fits on 2x A100)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-70B-Instruct",
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-70B-Instruct")
# Use like any other model
messages = [
{"role": "system", "content": "You are a helpful data engineering assistant."},
{"role": "user", "content": "Explain the medallion architecture."}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(input_ids, max_new_tokens=500)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Benchmark comparison:
benchmarks = {
"model": "Llama 3.1 405B vs GPT-4",
"mmlu": "88.6% vs 86.4%", # Llama wins
"humaneval": "89.0% vs 87.1%", # Llama wins
"math": "73.8% vs 76.6%", # GPT-4 wins
"overall": "Competitive"
}Deploying Open Source Models
Option 1: Self-Hosted with vLLM
# High-performance inference with vLLM
from vllm import LLM, SamplingParams
# Load model with optimizations
llm = LLM(
model="meta-llama/Llama-3.1-70B-Instruct",
tensor_parallel_size=2, # Across 2 GPUs
quantization="awq", # 4-bit quantization
gpu_memory_utilization=0.9
)
# Inference
sampling_params = SamplingParams(
temperature=0.7,
max_tokens=500
)
outputs = llm.generate(["Explain data lakehouse architecture"], sampling_params)
# vLLM benefits:
# - 3-5x throughput vs naive implementation
# - Continuous batching
# - PagedAttention for memory efficiency
# - Production-ready performanceOption 2: Azure AI Model Catalog
# Deploy open source models via Azure
from azure.ai.ml import MLClient
from azure.ai.ml.entities import ManagedOnlineEndpoint, ManagedOnlineDeployment
# Create endpoint
endpoint = ManagedOnlineEndpoint(
name="llama-3-1-70b-endpoint",
auth_mode="key"
)
# Deploy Llama from catalog
deployment = ManagedOnlineDeployment(
name="llama-deployment",
endpoint_name="llama-3-1-70b-endpoint",
model="azureml://registries/azureml-meta/models/Llama-3.1-70B-Instruct",
instance_type="Standard_NC96ads_A100_v4",
instance_count=1
)
ml_client.online_endpoints.begin_create_or_update(endpoint).result()
ml_client.online_deployments.begin_create_or_update(deployment).result()
# Benefits:
# - No infrastructure management
# - Azure security and compliance
# - Pay-per-hour, no upfront investment
# - Enterprise SLAs availableOption 3: Ollama for Development
# Simplest way to run open source models locally
# Install ollama
curl -fsSL https://ollama.ai/install.sh | sh
# Run Llama 3.1
ollama run llama3.1:70b
# Or use the API
curl http://localhost:11434/api/generate -d '{
"model": "llama3.1:70b",
"prompt": "Explain data mesh architecture"
}'# Python integration
import ollama
response = ollama.chat(
model='llama3.1:70b',
messages=[
{'role': 'user', 'content': 'What is Microsoft Fabric?'}
]
)
print(response['message']['content'])Open Source vs Proprietary: Decision Framework
decision_matrix = {
"use_open_source_when": [
"Data sensitivity requires on-premise",
"Cost optimization is priority at scale",
"Need full control over model behavior",
"Customization/fine-tuning is essential",
"Regulatory requirements mandate data locality"
],
"use_proprietary_when": [
"Need cutting-edge capabilities",
"Minimal infrastructure overhead desired",
"Rapid prototyping is priority",
"Small-medium scale operations",
"Need vendor support and SLAs"
],
"hybrid_approach": {
"description": "Best of both worlds",
"strategy": [
"Use proprietary APIs for prototyping",
"Evaluate open source for production",
"Route by use case requirements",
"Fine-tune open source for specialized tasks"
]
}
}
def recommend_approach(requirements: dict) -> str:
score_open = 0
score_proprietary = 0
if requirements.get("data_sensitivity") == "high":
score_open += 3
if requirements.get("monthly_requests", 0) > 1_000_000:
score_open += 2 # Cost advantage
if requirements.get("need_fine_tuning"):
score_open += 2
if requirements.get("need_latest_capabilities"):
score_proprietary += 2
if requirements.get("team_ml_expertise") == "low":
score_proprietary += 2
if score_open > score_proprietary:
return "open_source"
elif score_proprietary > score_open:
return "proprietary"
else:
return "hybrid"Cost Comparison
cost_comparison = {
"gpt_4o_api": {
"cost_per_1m_input": 2.50,
"cost_per_1m_output": 10.00,
"infrastructure": 0,
"total_100m_tokens": 625 # Mixed in/out
},
"llama_3_1_70b_self_hosted": {
"gpu_cost_per_hour": 4.50, # 2x A100
"tokens_per_hour": 500_000, # Optimized
"cost_per_1m_tokens": 9.00, # Compute only
"infrastructure_monthly": 500, # Storage, networking
"total_100m_tokens": 900 + 500 # First month
},
"llama_3_1_70b_azure_catalog": {
"cost_per_hour": 8.00, # Managed
"tokens_per_hour": 400_000,
"cost_per_1m_tokens": 20.00,
"infrastructure": 0,
"total_100m_tokens": 2000
}
}
# Key insight:
# Self-hosted is cheaper at high volume
# Break-even at ~50M tokens/month typicallyEnterprise Considerations
enterprise_considerations = {
"licensing": {
"llama_3_1": "Llama 3.1 Community License",
"restrictions": "700M MAU limit, certain use cases",
"commercial_use": "Allowed with conditions",
"action": "Review license for your use case"
},
"support": {
"proprietary": "Vendor support included",
"open_source": "Community + paid support options",
"huggingface_enterprise": "Enterprise support available"
},
"security": {
"self_hosted": "Full control, your responsibility",
"azure_catalog": "Azure security + your controls",
"api": "Trust vendor security"
},
"compliance": {
"data_residency": "Self-hosted enables any location",
"audit": "Self-hosted provides full audit capability",
"certifications": "Depends on hosting choice"
}
}Looking Ahead
2025 Open Source AI Predictions:
├── Quality gap continues to close
├── Specialized models proliferate
├── Deployment tooling matures
├── Enterprise adoption accelerates
├── Hybrid approaches become standard
└── Community innovation acceleratesOpen source AI is no longer a compromise - it’s a strategic option. Evaluate based on your specific requirements, not assumptions.