Tom9000 Q1_S 155B Parameters

VRAM Requirements for
TheProfessor-155b-GUFF-Q1-v02 Q1_S

To run TheProfessor-155b-GUFF-Q1-v02 locally at Q1_S quantization, you need at minimum 159.54 GB of GPU VRAM.

159.54 GB
Required VRAM
155 GB
File Size
8K tokens
Context Window
155B
Parameters
Estimated VRAM Required
159.54
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

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 (Q1_S) 155 GB
Context Overhead (8,192 tokens × 155B × 2 ÷ 1M) 2.54 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 159.54 GB

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TheProfessor-155b-GUFF-Q1-v02 — Other Quantizations

Q1_S Current
159.54 GB
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Frequently Asked Questions

Can I run TheProfessor-155b-GUFF-Q1-v02 Q1_S on a consumer GPU?
Running TheProfessor-155b-GUFF-Q1-v02 Q1_S locally requires 159.54 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 TheProfessor-155b-GUFF-Q1-v02?
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 Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.