Amai Gsu FP16 Unknown Parameters

VRAM Requirements for
SmolLM2-360M-Instruct-Q4_K_S-GGUF FP16

To run SmolLM2-360M-Instruct-Q4_K_S-GGUF locally at FP16 quantization, you need at minimum 2 GB of GPU VRAM.

2 GB
Required VRAM
0 GB
File Size
8K tokens
Context Window
Unknown
Parameters
Estimated VRAM Required
2
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 (FP16) 0 GB
Context Overhead (8,192 tokens × Unknown × 2 ÷ 1M) 0 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 2 GB

Try a Different Quantization

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SmolLM2-360M-Instruct-Q4_K_S-GGUF — Other Quantizations

FP16 Current
2 GB
Q4_0 0 GB file
2 GB
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Frequently Asked Questions

Can I run SmolLM2-360M-Instruct-Q4_K_S-GGUF FP16 on a consumer GPU?
Yes! At 2 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 SmolLM2-360M-Instruct-Q4_K_S-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 FP16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.