The Challenge: Small startups and research teams face a critical dilemma: how to provide cutting-edge AI coding assistance to their developers without breaking the bank on expensive cloud API costs or compromising on quality and security.

In this comprehensive guide, we'll explore how a small startup with 5-10 researchers can build a robust, hybrid AI coding infrastructure using Claude Code CLI, ChatGPT Codex, and OpenCode — all configured to seamlessly connect to both local LLMs running on Apple Silicon and NVIDIA workstations, as well as remote cloud APIs.

The Hybrid Architecture Approach

Our proposed solution leverages the best of both worlds: powerful local hardware for privacy-sensitive work and reduced API costs, combined with cloud services for scalability and access to the latest models. This hybrid approach provides flexibility, cost savings, and ensures your team always has access to AI assistance.

Component Breakdown

1. Hardware Infrastructure

The foundation of this cost-effective setup relies on strategic hardware investments:

• 5x NVIDIA RTX 5090 Workstations (32GB VRAM each) - Each researcher gets a dedicated station with 32GB VRAM, capable of running 7B-13B parameter models locally with excellent performance. Perfect for fine-tuning small to medium models (1B-8B) with QLoRA. The RTX 5090 provides outstanding inference speed at a fraction of the cost of professional-grade GPUs like A100s.
• 1x Dual RTX 6000 Pro Server (192GB total VRAM) - A shared server with 2x RTX 6000 Pro GPUs (96GB each) for training larger models (7B-70B) in full FP16 precision without quantization. This configuration handles multi-GPU distributed training and can run larger batch sizes for faster convergence.
• 2x Apple M3 Max Systems (512GB RAM) - These serve as shared inference servers for larger models. The M3's unified memory architecture allows running 70B+ parameter models efficiently. The 512GB configuration can handle multiple concurrent users running large models.

2. AI Coding Clients (Hybrid Configuration)

We deploy three AI coding clients, each configured to intelligently route requests between local LLMs and cloud APIs based on task complexity:

• Claude Code CLI - Anthropic's powerful command-line interface for code generation, debugging, and refactoring. Configured with dual endpoints: connects to local Llama 3.1 70B running on Apple M3 Max via OpenAI-compatible API for routine tasks, and falls back to cloud-based Claude 3.5 Sonnet for complex architectural decisions. Perfect for terminal-based workflows and automation scripts.
• ChatGPT Codex - OpenAI's specialized code model accessible through their API. Configured to route simple completions and code suggestions to local DeepSeek Coder (running on RTX 5090 workstations), while leveraging cloud-based GPT-4 Turbo for complex problem-solving, legacy code analysis, and multi-language translation tasks. Integrates seamlessly with VS Code and JetBrains IDEs.
• OpenCode - Open-source AI coding assistant that provides complete flexibility in model selection. Configured to work with both local models (Llama, DeepSeek Coder, Qwen 2.5) running on your infrastructure and cloud APIs (Claude, GPT-4, Gemini). Offers vendor-neutral architecture, avoiding lock-in while maintaining access to the latest capabilities. Ideal for experimentation and custom workflows.

Our Real-World Experience: Hundreds of Hours with These Tools

After extensive hands-on experience with all three AI coding assistants, here's what we've learned from hundreds of hours of real-world usage:

Bottom line: Each tool has its sweet spot. Start prototypes with OpenCode, do your heavy development with Claude Code, and bring in Codex when you hit those truly challenging system-level problems that need expert-level troubleshooting.

Fine-Tuning Models on Your Hardware: Practical Examples

Beyond inference, this hardware setup enables you to fine-tune models locally for domain-specific tasks. Here's how we configure training for different model sizes using our train_unified.py script:

Small Model Training (1B-4B) on RTX 5090

The RTX 5090's 32GB VRAM is perfect for fine-tuning small to medium models with QLoRA (4-bit quantization):

# Small models (1-4B): Conservative, stable training on RTX 5090
SMALL_MODEL_CONFIG = {
    "lora_r": 64,
    "lora_alpha": 128,
    "lora_dropout": 0.1,
    "learning_rate": 3e-5,  # Very conservative
    "warmup_ratio": 0.3,  # Long warmup
    "max_grad_norm": 0.3,  # Aggressive clipping
    "weight_decay": 0.01,
    "gradient_accumulation_steps": 2,
    "use_device_map": False,  # Direct GPU placement for stability
    "use_qlora": True,  # 4-bit quantization with NF4
    "use_bf16": True,  # BFloat16 precision
    "batch_size": 12,  # Can fit comfortably in 32GB
}

# Example: Fine-tune Qwen2.5-4B on RTX 5090
python train_unified.py --models qwen3-4b --folds 0 1 2 --train-csv data/train.csv

# Example: Fine-tune Llama 3.2-3B on RTX 5090
python train_unified.py --models llama3-3b --folds 0 1 2

Large Model Training (7B-70B) on Dual RTX 6000 Pro

For larger models, the dual RTX 6000 Pro configuration (192GB total VRAM) handles 7B-70B models with full FP16 precision or aggressive QLoRA:

# Large models (7B+): More aggressive, multi-GPU training
LARGE_MODEL_CONFIG = {
    "lora_r": 64,
    "lora_alpha": 128,
    "lora_dropout": 0.05,
    "learning_rate": 2e-4,  # Higher LR for larger models
    "warmup_ratio": 0.05,
    "max_grad_norm": 0.5,
    "weight_decay": 0.01,
    "gradient_accumulation_steps": 4,
    "use_device_map": True,  # device_map="auto" for multi-GPU
    "use_qlora": True,  # High precision QLoRA
    "use_bf16": True,
    "batch_size": 8,  # Fits in 192GB with room for gradients
}

# Example: Fine-tune Llama 3.1-8B on dual RTX 6000 Pro
python train_unified.py --models llama-3.1-8b --folds 0 1 2

# Example: Fine-tune DeepSeek-Math-7B
python train_unified.py --models deepseek-math-7b --folds 0 1 2

# Example: Train all Qwen family models (4B + 8B)
python train_unified.py --family qwen --folds 0 1 2

Intelligent Model Loading for Different Hardware

Our training script automatically adapts to available VRAM:

def build_model(config: Dict, n_classes: int, tokenizer):
    """Build model with appropriate configuration based on size"""

    is_small = config["size"] == "small"
    use_device_map = config.get("use_device_map", not is_small)

    # Precision selection (BF16 > FP16 > FP32)
    compute_dtype, _, _ = resolve_precision_settings()

    # QLoRA config for memory efficiency
    if config.get('use_qlora', False):
        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",  # NormalFloat4
            bnb_4bit_use_double_quant=True,  # Double quantization
            bnb_4bit_compute_dtype=compute_dtype,
        )

    # Load model with auto device mapping for large models
    model = AutoModelForSequenceClassification.from_pretrained(
        config['name'],
        num_labels=n_classes,
        quantization_config=quantization_config,
        torch_dtype=compute_dtype,
        device_map="auto" if use_device_map else None,  # Multi-GPU support
        use_cache=False,
        trust_remote_code=config.get('trust_remote_code', False),
    )

    # For small models on RTX 5090: direct CUDA placement (more stable)
    if not use_device_map and torch.cuda.is_available():
        model = model.to("cuda")

    return model

Real-world training times: Fine-tuning Llama 3.1-8B (5 epochs) takes ~6 hours on dual RTX 6000 Pro vs. ~4 hours on A100. The 3x cost difference far outweighs the 1.5x time difference for most startup use cases.

3. Local LLM Deployment (On-Premises Inference)

Running models locally is the key to cost savings. All three AI coding clients (Claude Code CLI, ChatGPT Codex, and OpenCode) connect to these local endpoints:

• Llama 3.1 70B - Deployed on Apple M3 Max servers using llama.cpp with Metal acceleration. Excellent for general coding tasks and code review. All three clients can route to this model via OpenAI-compatible API endpoint (typically port 8080).
• DeepSeek Coder 33B - Specialized for code generation, runs efficiently on RTX 5090 workstations via vLLM. Outstanding performance on code completion and bug fixing. Exposed as a local API endpoint that all clients can access.
• Qwen 2.5 Coder - Deployed on Apple M3 Max systems. Strong multilingual support, particularly good for Python, JavaScript, and systems programming. Available to all clients through the shared server infrastructure.
• Inference Serving Architecture - vLLM on RTX 5090s and llama.cpp on M3 Max expose OpenAI-compatible API endpoints. This allows Claude Code CLI, ChatGPT Codex, and OpenCode to treat local models identically to cloud APIs, enabling seamless switching between local and remote resources.

4. Cloud Services (Strategic Use via All Clients)

All three AI coding clients (Claude Code CLI, ChatGPT Codex, and OpenCode) are configured to access cloud APIs strategically for specific scenarios:

• Claude 3.5 Sonnet - Accessed via Claude Code CLI (primary) and OpenCode (secondary). Used for complex architectural decisions, comprehensive code reviews, and when you need the absolute best reasoning capability. The clients automatically escalate to this when local models can't handle the complexity.
• GPT-4 Turbo - Accessed via ChatGPT Codex (primary) and OpenCode (secondary). Fallback option for specific tasks where it excels: certain programming languages, legacy code analysis, and multi-language translation. Smart routing sends complex requests here after local attempts.
• Gemini 1.5 Pro - Accessible through OpenCode for long-context scenarios (analyzing entire codebases, processing large documentation sets up to 1M tokens). All clients can route ultra-long-context requests here when needed.

Important: The beauty of this hybrid setup is that developers use the same familiar clients (Claude Code CLI, ChatGPT Codex, OpenCode) regardless of whether requests are handled locally or in the cloud. The intelligent routing happens transparently in the background.

How the Hybrid Connection Works

Each AI coding client is configured with multiple endpoint URLs and smart routing logic:

Cost Analysis: Why This is Affordable

ROI Analysis

Compare this to a cloud-only approach:

• Heavy Claude API usage (5 developers, 2M tokens/day each): $3,000-5,000/month
• GPT-4 API equivalent usage: $4,000-7,000/month
• Our hybrid approach (local + cloud with smart routing): $450-750/month + hardware amortized over 3 years (~$1,240/month) = ~$1,700-2,000/month total

Payback period: With moderate to heavy API usage, the hardware investment pays for itself in 9-14 months. After that, you're only paying operational costs (~$600/month for electricity + light cloud usage).

Implementation Roadmap

Week 1-2: Hardware Setup

• Procure and set up 5x RTX 5090 workstations (32GB each)
• Build dual RTX 6000 Pro training server (192GB total VRAM)
• Configure Mac Studio M3 Max systems as shared inference servers
• Set up local network with adequate bandwidth (10GbE recommended for multi-GPU training)
• Install base OS, CUDA 12.x, and PyTorch 2.x on all systems
• Configure NVLink for RTX 6000 Pro GPUs (if using NVLink bridge)

Week 3-4: Software Infrastructure

• Deploy vLLM on RTX 5090 systems for local model serving (inference)
• Set up llama.cpp with Metal acceleration on M3 Max
• Download and quantize models (Llama 3.1 70B, DeepSeek Coder, Qwen)
• Configure OpenAI-compatible API endpoints for all local models
• Set up fine-tuning environment on dual RTX 6000 Pro (PyTorch, Transformers, PEFT, bitsandbytes)
• Deploy train_unified.py and test with a small model (Qwen 3B or Llama 3.2-3B)
• Configure TensorBoard for training monitoring

Week 5-6: Client Configuration

• Configure Claude Code CLI with dual endpoints (local M3 Max LLMs + cloud Claude API)
• Set up ChatGPT Codex with routing to local RTX 5090 models and cloud GPT-4
• Install and configure OpenCode with multi-model support (local + cloud)
• Implement intelligent routing logic (local-first, cloud fallback based on complexity)
• Configure IDE integrations (VS Code, JetBrains) for all three clients

Week 7-8: Testing & Optimization

• Benchmark performance across different coding tasks
• Fine-tune model selection and routing rules
• Train team on best practices
• Monitor and optimize costs

Best Practices & Tips

Smart Routing Strategy

Implement intelligent routing to maximize local usage while maintaining quality:

• Simple completions: Always use local models (DeepSeek Coder on RTX 5090)
• Code review & refactoring: Use local Llama 3.1 70B first, escalate to Claude if needed
• Complex architecture decisions: Go straight to Claude 3.5 Sonnet
• Long-context analysis: Use Gemini 1.5 Pro (up to 1M tokens)

Privacy & Security

• Keep proprietary code analysis on local models only
• Implement request sanitization before cloud API calls
• Use VPN for all cloud API access
• Regular security audits of API keys and access logs

Model Updates

• Schedule monthly model updates during off-hours
• Maintain a model registry with performance benchmarks
• A/B test new models before full deployment
• Keep 2-3 model versions for rollback capability

Conclusion

Building a hybrid AI coding infrastructure isn't just about saving money—it's about building a sustainable, scalable foundation for your startup's development team. By strategically combining local hardware (RTX 5090s, RTX 6000 Pro, M3 Max systems), open-source models, and cloud services, you can provide world-class AI assistance to your developers at a fraction of the cost of cloud-only or enterprise GPU solutions.

The $44,500 upfront hardware investment pays for itself in 9-14 months compared to heavy cloud API usage, and provides your team with capabilities that go beyond just inference:

• Fast local inference - 5-10x faster than cloud APIs for everyday coding tasks
• Fine-tuning capability - Train domain-specific models (1B-70B) on your own hardware
• Full control - Protect your intellectual property and eliminate concerns about API rate limits
• Future-proof - Adapt to new models and techniques as they emerge
• Cost predictability - Fixed hardware costs + minimal cloud usage vs. unpredictable API bills

For small startups and research teams, this hybrid approach represents the sweet spot: professional-grade AI coding assistance and fine-tuning capabilities without enterprise-grade costs. You get 80% of the performance at 25-30% of the price compared to enterprise GPUs like A100s and H100s.