Ysingh Aiml Q8_0 1.1B Parameters

VRAM Requirements for
tinyllama-alpaca-lora-gguf Q8_0

To run tinyllama-alpaca-lora-gguf locally at Q8_0 quantization, you need at minimum 3.19 GB of GPU VRAM.

3.19 GB
Required VRAM
1.17 GB
File Size
8K tokens
Context Window
1.1B
Parameters
Estimated VRAM Required
3.19
GB
Consumer Friendly
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $299 – $349

RTX 4060 (8GB)

8 GB

Perfect entry-level GPU. Handles small quantised models with ease.

Balanced $549 – $599

RTX 4070 (12GB)

12 GB

Excellent performance-per-dollar for running sub-7B models at Q8.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Overkill for this size — plenty of headroom for bigger models.

📊 VRAM Calculation Breakdown

Model File Size (Q8_0) 1.17 GB
Context Overhead (8,192 tokens × 1.1B × 2 ÷ 1M) 0.018 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 3.19 GB

Try a Different Quantization

Use the interactive calculator to compare tinyllama-alpaca-lora-gguf across all available formats.

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tinyllama-alpaca-lora-gguf — Other Quantizations

Q4_K_M 0.62 GB file
2.64 GB
Q5_K_M 0.76 GB file
2.78 GB
Q8_0 Current
3.19 GB
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Frequently Asked Questions

Can I run tinyllama-alpaca-lora-gguf Q8_0 on a consumer GPU?
Yes! At 3.19 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run tinyllama-alpaca-lora-gguf?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q8_0 quality good enough for production?
Q8_0 produces near-lossless quality compared to FP16. It's widely used in production deployments where quality is critical and you can afford the extra VRAM.