Tomngdev BF16 149B Parameters

VRAM Requirements for
Step-3.5-Flash-REAP-149B-A11B-GGUF BF16

To run Step-3.5-Flash-REAP-149B-A11B-GGUF locally at BF16 quantization, you need at minimum 302.44 GB of GPU VRAM.

302.44 GB
Required VRAM
298 GB
File Size
8K tokens
Context Window
149B
Parameters
Estimated VRAM Required
302.44
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 142.4 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 142.4 GB short
Balanced $60,000+

2× H100 80GB NVLink

160 GB HBM3

NVLink bridge enables unified 160GB VRAM pool.

Ultimate $3,000,000+

NVIDIA GB200 NVL72

1.4 TB HBM3e

Next-gen Grace Blackwell Superchip. Built for frontier models.

📊 VRAM Calculation Breakdown

Model File Size (BF16) 298 GB
Context Overhead (8,192 tokens × 149B × 2 ÷ 1M) 2.441 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 302.44 GB

Try a Different Quantization

Use the interactive calculator to compare Step-3.5-Flash-REAP-149B-A11B-GGUF across all available formats.

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Step-3.5-Flash-REAP-149B-A11B-GGUF — Other Quantizations

Q2_K 46.56 GB file
51 GB
Q3_K_L 65.19 GB file
69.63 GB
Q3_K_M 65.19 GB file
69.63 GB
Q3_K_S 65.19 GB file
69.63 GB
Q4_K_M 83.81 GB file
88.25 GB
Q4_K_S 83.81 GB file
88.25 GB
Q5_K_M 102.44 GB file
106.88 GB
Q5_K_S 102.44 GB file
106.88 GB
Q6_K 121.06 GB file
125.5 GB
FP4 149 GB file
153.44 GB
Q8_0 158.31 GB file
162.75 GB
BF16 Current
302.44 GB
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Frequently Asked Questions

Can I run Step-3.5-Flash-REAP-149B-A11B-GGUF BF16 on a consumer GPU?
Running Step-3.5-Flash-REAP-149B-A11B-GGUF BF16 locally requires 302.44 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 Step-3.5-Flash-REAP-149B-A11B-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 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.