Mid-Range
Mid-RangePower AI Coding
Mid-Range AI Workstation
$1,228
16 GB VRAM320W TDP~30dB8 components
Inside This Build
Components
| Category | Component | Price | Rationale | Buy |
|---|---|---|---|---|
| gpu |  NVIDIA GeForce RTX 4060 Ti 16GB | $449 | 16GB VRAM is the sweet spot for most AI workloads. Runs 7-14B models at high quality, and 32B models at lower quantizations. Ada Lovelace efficiency means low power and quiet. | |
| cpu |  AMD Ryzen 5 7600 | $179 | 6-core Zen 4 with DDR5 support. Fast single-thread for system responsiveness during inference. | |
| motherboard |  MSI B650M Mortar WiFi | $159 | AM5 board with WiFi 6E. PCIe 4.0 x16 for GPU. Room for CPU upgrades to Ryzen 9. | |
| ram | 32GB DDR5-5600 (2x16GB) | $89 | DDR5 for AM5 platform. 32GB is adequate for 16GB GPU workloads. Expandable to 64GB. | |
| storage |  2TB Samsung 990 EVO NVMe | $129 | 2TB for a large model library. Fast sequential reads for model loading. | |
| psu |  Corsair RM650x 650W 80+ Gold | $89 | 650W covers the 4060 Ti with comfortable headroom. Fully modular. | |
| case | Fractal DesignFractal Design North Mini | $99 | Premium mATX case with excellent airflow and low noise. Wood panel aesthetic. | |
| cooler | ThermalrightThermalright Peerless Assassin 120 | $35 | Dual-tower cooler that handles the 7600 easily and stays whisper-quiet. | |
| Total | $1,228 | |||
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What This Runs
AI models tested on this build's hardware.
| Model | Quant | Speed |
|---|---|---|
| Llama 3.1 8B Instruct | Q8_0 | 55 tok/s |
| Qwen 2.5 Coder 32B Instruct | Q3_K_M | 12 tok/s |
| Phi-3 Medium 14B Instruct | Q5_K_M | 28 tok/s |
| Codestral 22B | Q3_K_M | 18 tok/s |
| Gemma 2 9B Instruct | Q5_K_M | 40 tok/s |
| Stable Diffusion XL 1.0 | FP16 | — |
| FLUX.1 Dev | Q4_K_M | — |
| nomic-embed-text v1.5 | FP16 | — |
AI Builder Workflow: Model Fine-Tuning & Training
Fine-tune language models locally with QLoRA, LoRA, and full fine-tuning. Train custom adapters for domain-specific tasks without sending proprietary data to third-party APIs. VRAM requirements scale with model size and method: QLoRA fine-tuning a 7B model fits in 16GB, while full fine-tuning of 32B models needs 48GB+. System RAM matters — gradient checkpointing and dataset loading use 2-4x the model's VRAM in system memory.
Tools in this workflow
AxolotlUnslothHugging Face TRLLLaMA FactoryPEFT
Concurrent VRAM Usage
10 GB of 16 GB
6 GB headroom for additional workloads
API Cost Replacement
If you're paying ~$200/month for cloud API access, this build pays for itself in 10 months.
Based on OpenAI fine-tuning API pricing ($8/1M training tokens, 3-5 fine-tuning runs/month on 50K-row datasets). Local fine-tuning is unlimited iterations with zero per-token cost. Electricity cost ~$15/mo at 6hr/day GPU usage during training. Mid-Range Workstation at ~$1,400. Privacy advantage is the real differentiator — proprietary data never leaves your machine.
Upgrade Path
The big jump is GPU: RTX 4070 Ti Super ($779) for 2x the bandwidth at same 16GB, or RTX 4090 ($1799) for 24GB VRAM. The AM5 platform supports future CPU upgrades.
Target Use Cases
ChatCodingAI CodingImage Gen
Last updated: 2026-03-01.