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Quick Answer: hmellor/tiny-random-LlamaForCausalLM requires a minimum of 4GB VRAM for Q4 quantization. Compatible with 5 GPUs including NVIDIA RTX 6000 Ada. Expected speed: ~93 tokens/sec on NVIDIA RTX 6000 Ada. Plan for 32GB system RAM and 100GB of fast storage for smooth local inference.
This model delivers strong local performance when paired with modern GPUs. Use the hardware guidance below to choose the right quantization tier for your build.
Start with at least 4GB of VRAM for Q4 inference. Scale to higher quantizations as your hardware grows, and pick a build below that fits your budget and throughput goals.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 4GB (Q4) | 7GB (Q8) | 14GB (FP16) |
| RAM | 16GB | 32GB | 64GB |
| Disk | 50GB | 100GB | - |
| Model size | 4GB (Q4) | 7GB (Q8) | 14GB (FP16) |
| CPU | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) |
See compatible GPUs →
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Common questions about running hmellor/tiny-random-LlamaForCausalLM locally
Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).
Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.
Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.