Beit Tigre A I BF16 2B Parameters

VRAM Requirements for
gemma-4-E2B-sft-tran-tigre BF16

To run gemma-4-E2B-sft-tran-tigre locally at BF16 quantization, you need at minimum 6.03 GB of GPU VRAM.

6.03 GB
Required VRAM
4 GB
File Size
8K tokens
Context Window
2B
Parameters
Estimated VRAM Required
6.03
GB
Consumer Friendly
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $350 – $500

RTX 3080 (10GB)

10 GB

Used market gem. Tight on VRAM but viable for this workload.

Balanced $699 – $799

RTX 4070 Ti (12GB)

12 GB

Strong inference GPU. Handles 7-13B models comfortably.

Ultimate $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Best consumer GPU. Breeze through 13B models at any quantization.

📊 VRAM Calculation Breakdown

Model File Size (BF16) 4 GB
Context Overhead (8,192 tokens × 2B × 2 ÷ 1M) 0.033 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 6.03 GB

Try a Different Quantization

Use the interactive calculator to compare gemma-4-E2B-sft-tran-tigre across all available formats.

Open Live Calculator →

gemma-4-E2B-sft-tran-tigre — Other Quantizations

Q4_K_M 1.13 GB file
3.16 GB
Q5_K_M 1.38 GB file
3.41 GB
Q8_0 2.13 GB file
4.16 GB
BF16 Current
6.03 GB
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Frequently Asked Questions

Can I run gemma-4-E2B-sft-tran-tigre BF16 on a consumer GPU?
Yes! At 6.03 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 gemma-4-E2B-sft-tran-tigre?
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 BF16 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.