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Are you looking for a pre-built gaming PC to run 70B parameterized large language models? Check out our findings.
AI is the current buzzword everywhere. It’s inside smartphones, laptops, PCs, cameras, and more. But that’s just the use case side of things. What if you’re the kind of person who likes to experiment with tools like LM Studio or Ollama, train AI models on your own dataset, or run them locally for specific needs? A gaming PC alone won’t cut it. You’ll either have to order AWS clusters or invest in servers that can handle the workload.
What if you prefer privacy and a Docker container environment? What if DeepSeek and other LLMs intrigue you, and you want to run them locally? Well, our article has you covered. Today, we’re discussing hassle-free, enthusiast-grade prebuilts that will help you run up to 70B LLMs—of course, with quantization. We won’t mention costs here due to the significant discrepancies in GPU pricing and availability, but you get the drill. Lastly, most of these prebuilts will be configurable; before placing orders, clarify with the manufacturer/integrators.
1. Corsair One i500
Key Specifications
- Intel Core i9-14900K
- NVIDIA GeForce RTX 4090 with 24GB GDDR6X VRAM
- RAM: 64GB DDR5
- Storage: 2TB NVMe SSD
Verdict
A setup like this is recommended for an absolute high-end use case: For AI-specific workloads, the RTX 4090 within this can run.
- DeepSeek models up to 70B with aggressive quantization (4-bit or lower).
- Smaller models (7B, 13B, 33B) with int8 or int4 quantization using GGUF, GPTQ, or AWQ.
- 67B/70B models require CPU offloading or optimized loaders like vLLM/ExLlama2 with quantized weights.
Be assured, everything will be smooth sailing when it comes to gaming.
2. Maingear MG-1
Key Specifications
- AMD Ryzen 9 7950X or Intel Core i9-14900K (configurable)
- GPU options range from RTX 4070 to RTX 4090 or AMD Radeon options
- 32GB to 128GB DDR5 (configurable DDR5 RAM)
- Multiple NVMe SSD options
Verdict
Depending on your budget, the MG-1 can be highly customized for specific AI-related workloads or general day-to-day gaming. With an RTX 4090, the same Deepseek appliance can be said here, which we mentioned in the Corsair One i500. If you choose a GPU with 12 GB VRAM, the smaller models (7B, 13B with quantization) will best suit you.
3. iBuyPower RDY Y70 B03
Key Specifications
- Intel Core Ultra 9 285K CPU
- NVIDIA GeForce RTX 5090 with 32GB GDDR7 VRAM
- 64GB DDR5
- 4TB NVMe SSD
Verdict
Aside from the CPU, the RTX 5090 is currently the most powerful you can get for desktop GPUs. With that said, the 32 GB VRAM will help you run:
- Small models (e.g., Mistral 7B, Phi-3, Gemma 7B) at full precision (fp16)
- 70B models (e.g., Llama 3 70B, DeepSeek 67B) at 6-bit or even 8-bit quantization with minimal offloading.
Gaming, assuming 4K is your target resolution, you are good to go with anything you throw at it.
4. Lenovo Legion Tower 7i (Gen 8)
Key Specifications
- Up to Intel Core i9-14900K
- Up to NVIDIA RTX 4080 Super (16GB VRAM)
- Up to 128GB DDR5
- Storage- 2TB SSD
Verdict
The Lenovo Legion Tower 7i is one of the most configurable options we’ve seen. Users can opt in for parts according to their budget. When considering the 4080 Super GPU. The following AI models can be run:
- Mistral 7B, Gemma 7B, Llama 2 13B
- Llama 3 33B, DeepSeek 33B Works with 4-bit quantization but may need offloading.
- Llama 3 70B and DeepSeek 67B can be run with CPU offloading and 4-bit quantization
At 2025, the 4080 Super is more of a mid-high-end gaming GPU; you must turn down a few settings to sustain a full RT experience.
5. Legion Tower 7i (Gen 10)
Key Specifications
- Up to Intel Core Ultra 9 285K
- Up to NVIDIA RTX 4080 Super (16GB VRAM)
- Up to 128GB DDR5
- Storage- 2TB SSD
Verdict
It’s basically the same as the Gen 8 model above; it just uses the Intel Arrowlake platform. Some AI-related tasks may benefit from Intel’s AVX VNNI extensions found within Arrowlake. Rest assured, everything would be the same as in the 4080 Super for gaming.
Selecting either of these configurations will provide a smooth gaming experience while also enabling you to run smaller LLMs on lower VRAM GPUs. The higher VRAM lets you run more parameters with greater precision, resulting in higher accuracy and faster output.
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