Local LLM Setup: Complete Guide for Self-Hosted AI

Why Run LLMs Locally?

Running LLMs locally gives you complete control over your AI infrastructure:

Data privacy: Your data never leaves your servers

No API costs: Pay for hardware once, run unlimited inference

Customization: Fine-tune models for your specific domain

Low latency: No network round-trips to external APIs

No rate limits: Scale to your hardware capacity

Hardware Requirements

Minimum Setup (7B models)

GPU: NVIDIA RTX 3090 (24GB VRAM) or RTX 4090

RAM: 32GB

Storage: 100GB NVMe SSD

Cost: ~$1,500

Production Setup (70B models)

GPU: 2x NVIDIA A100 80GB or 4x A6000 48GB

RAM: 128GB

Storage: 1TB NVMe SSD

Cost: ~$15,000-30,000

Cloud Alternative

RunPod: A100 80GB at $1.99/hr

Lambda Labs: A100 at $1.10/hr

Vast.ai: Budget option starting at $0.50/hr

Setting Up vLLM

vLLM is the fastest open-source LLM serving engine. It uses PagedAttention for 24x higher throughput than naive implementations.

``bash

pip install vllm

python -m vllm.entrypoints.openai.api_server \


    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 2 \
    --max-model-len 8192 \
    --port 8000

Now you have an OpenAI-compatible API endpoint at http://localhost:8000/v1.

`python


from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-70B-Instruct",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

`Fine-Tuning with QLoRA`

QLoRA lets you fine-tune large models on consumer GPUs by quantizing the base model to 4-bit and training only small adapter layers.

`python


from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    quantization_config=bnb_config,
)
lora_config = LoraConfig(
    r=16, lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)

`Training Data Format`

`jsonl


{"messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "What is X?"}, {"role": "assistant", "content": "X is..."}]}

Turkish Language Models

For Turkish-specific tasks, consider:

Trendyol LLM: Fine-tuned on Turkish e-commerce data

TURNA: Turkish language model from ITU

Multilingual models: Llama 3.1, Qwen 2.5, Mistral Nemo (all support Turkish)

Fine-tuning any multilingual model on 10K Turkish examples dramatically improves performance.

Building an API Marketplace

Once you have models running, you can offer them as a service:

API Gateway: Nginx + rate limiting

Authentication: API keys with usage tracking

Billing: Token-based pricing

Monitoring: Prometheus + Grafana dashboards

Load Balancing: Multiple GPU workers behind a reverse proxy

Cost Comparison

| Approach | Monthly Cost (1M tokens/day) |

|----------|------------------------------|

| OpenAI GPT-4 | $900 |

| Claude 3.5 Sonnet | $450 |

| Self-hosted Llama 70B (A100) | $150 |

| Self-hosted Llama 8B (RTX 4090) | $40 |

Self-hosting pays for itself within 3-6 months at moderate usage.

Our Solution

Our Local LLM Platform includes everything above packaged into a production-ready system: vLLM serving, QLoRA fine-tuning pipelines, API marketplace with billing, and monitoring dashboards. Available in Starter ($99) and Professional ($299) tiers.