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RUNLOCALAI · v38
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  6. /Ch. 14
Hardware Planning for Local AI

14. Budget Build: Mid-Range $800-1200

Chapter 14 of 20 · 20 min
KEY INSIGHT

The RTX 4080 16GB balances cost and capability—$700 for the 4070 Ti saves $300 but loses 4GB VRAM and significant 40B+ model capacity. ```bash # Full system benchmark for AI workloads python3 << 'EOF' import torch import time print(f"CUDA available: {torch.cuda.is_available()}") if torch.cuda.is_available(): print(f"GPU: {torch.cuda.get_device_name(0)}") print(f"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB") # Inference simulation start = time.time() for _ in range(100): x = torch.randn(4096, 4096, device='cuda') y = x @ x.T torch.cuda.synchronize() elapsed = time.time() - start print(f"Matrix operations: {elapsed:.2f}s ({100/elapsed:.1f} ops/sec)") EOF ```

The mid-range build provides capacity for 13B models with headroom for 33B through quantization. This tier represents the practical sweet spot for serious users.

Recommended Configuration

Component Model Price
GPU RTX 4080 16GB $1000
CPU Ryzen 7 7700X $250
Motherboard B650 $140
RAM 2x16GB DDR5-6000 $120
Storage 1TB NVMe Gen4 $90
PSU 850W 80+ Gold $110
Case Mid-tower $80
Total $1790

Budget compromise option:

Component Lower Option Price
GPU RTX 4070 Ti 12GB $700
RAM 2x16GB DDR5-5600 $100
Storage 512GB $50
Total $1450

Performance Expectations

With RTX 4080 16GB:

  • Llama 3 8B FP16: 35-45 tokens/sec
  • Llama 3 13B INT4: 25-30 tokens/sec
  • Mixtral 8x7B: 18-20 tokens/sec
  • Llama 3 70B INT4: 10-12 tokens/sec (stretch)

With RTX 4070 Ti 12GB:

  • Llama 3 8B FP16: 30-38 tokens/sec
  • Llama 3 13B INT4: 20-25 tokens/sec
  • Llama 3 70B INT4: Will not fit

Motherboard Selection

B650 motherboards for Ryzen 7000 series:

Board PCIe Gen M.2 Slots USB Ports Price
B650M DS3H 4.0 2 8 $100
B650M Steel Legend 5.0 2 10 $150
B650E Taichi 5.0 4 14 $300

PCIe 5.0 matters for future GPU upgrades but current RTX 4000 series uses PCIe 4.0.

Cooling Requirements

The RTX 4080 generates significant heat:

# Thermal testing before enclosure
nvidia-smi -q -d temperature | grep "GPU Current Temp"
# Target: Under 80°C under load

# If too hot:
# - Add case intake fans (2x 140mm recommended)
# - Undervolt GPU: nvidia-smi -pl 320
# - Check case airflow direction
EXERCISE

Compare two configurations: Option A (RTX 4080 16GB, $1790 total) versus Option B (RTX 4070 Ti 12GB, $1450 total). Calculate the cost per additional VRAM gigabyte and tokens per second difference.

← Chapter 13
Budget Build: Entry-Level Under $500
Chapter 15 →
Budget Build: High-Performance $2000-3000