Bobchenyx Q1_S 236B Parameters

VRAM Requirements for
DeepSeek-V3-0324-MLA-GGUF Q1_S

To run DeepSeek-V3-0324-MLA-GGUF locally at Q1_S quantization, you need at minimum 241.87 GB of GPU VRAM.

241.87 GB
Required VRAM
236 GB
File Size
8K tokens
Context Window
236B
Parameters
Estimated VRAM Required
241.87
GB
Cluster Required
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

⚠️ 81.9 GB short
Budget $40,000+

4× A100 40GB Cluster

160 GB

Distributed inference across 4 A100s. Minimum viable cluster.

⚠️ 81.9 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 (Q1_S) 236 GB
Context Overhead (8,192 tokens × 236B × 2 ÷ 1M) 3.867 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 241.87 GB

Try a Different Quantization

Use the interactive calculator to compare DeepSeek-V3-0324-MLA-GGUF across all available formats.

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DeepSeek-V3-0324-MLA-GGUF — Other Quantizations

Q2_K 73.75 GB file
79.62 GB
Q4_K_M 132.75 GB file
138.62 GB
Q1_M 236 GB file
241.87 GB
Q1_S Current
241.87 GB
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Frequently Asked Questions

Can I run DeepSeek-V3-0324-MLA-GGUF Q1_S on a consumer GPU?
Running DeepSeek-V3-0324-MLA-GGUF Q1_S locally requires 241.87 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 DeepSeek-V3-0324-MLA-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 Q1_S quality good enough for production?
Q1_S is suitable for specialized use cases. Check community benchmarks for specific quality metrics.