Aresrolls FP16 26B Parameters

VRAM Requirements for
gemma-4-nutrition-tuned FP16

To run gemma-4-nutrition-tuned locally at FP16 quantization, you need at minimum 54.43 GB of GPU VRAM.

54.43 GB
Required VRAM
52 GB
File Size
8K tokens
Context Window
26B
Parameters
Estimated VRAM Required
54.43
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 6.4 GB short
Budget $3,200 – $4,000

2× RTX 4090 (48GB) + aggressive quant

48 GB

Use a lower quantization to fit. Viable for testing at this scale.

Balanced $15,000 – $22,000

NVIDIA A100 80GB PCIe

80 GB HBM2e

Single-card 80GB. Industry-standard for large model inference.

Ultimate $25,000 – $40,000

NVIDIA H100 80GB SXM5

80 GB HBM3

State-of-the-art inference. 3× the bandwidth of A100.

📊 VRAM Calculation Breakdown

Model File Size (FP16) 52 GB
Context Overhead (8,192 tokens × 26B × 2 ÷ 1M) 0.426 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 54.43 GB

Try a Different Quantization

Use the interactive calculator to compare gemma-4-nutrition-tuned across all available formats.

Open Live Calculator →

gemma-4-nutrition-tuned — Other Quantizations

Q4_0 14.63 GB file
17.06 GB
FP16 Current
54.43 GB
Advertisement Zone

Frequently Asked Questions

Can I run gemma-4-nutrition-tuned FP16 on a consumer GPU?
Running gemma-4-nutrition-tuned FP16 locally requires 54.43 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
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-nutrition-tuned?
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.