Verdugie BF16 27B Parameters

VRAM Requirements for
STEM-Oracle-27B BF16

To run STEM-Oracle-27B locally at BF16 quantization, you need at minimum 56.44 GB of GPU VRAM.

56.44 GB
Required VRAM
54 GB
File Size
8K tokens
Context Window
27B
Parameters
Estimated VRAM Required
56.44
GB
Data Centre Class
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 8.4 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 (BF16) 54 GB
Context Overhead (8,192 tokens × 27B × 2 ÷ 1M) 0.442 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 56.44 GB

Try a Different Quantization

Use the interactive calculator to compare STEM-Oracle-27B across all available formats.

Open Live Calculator →

STEM-Oracle-27B — Other Quantizations

Q4_K_M 15.19 GB file
17.63 GB
Q6_K 21.94 GB file
24.38 GB
Q8_0 28.69 GB file
31.13 GB
BF16 Current
56.44 GB
Advertisement Zone

Frequently Asked Questions

Can I run STEM-Oracle-27B BF16 on a consumer GPU?
Running STEM-Oracle-27B BF16 locally requires 56.44 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 STEM-Oracle-27B?
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.