Welcome to the first entry of Infradiaries. To kick things off, I wanted to share a “lab report” on my recent attempt to deploy the DeepSeek model family. If you’ve ever tried to run Large Language Models (LLMs) on consumer or prosumer hardware, you know the biggest enemy isn’t the code—it’s the VRAM Wall.
I tested three different models on our local stack (featuring an RTX A6000 48GB and a secondary 24GB server). Here is the raw truth about what it takes to select and run these modules.
🔍 The Research: 3 Models, 3 Failures, 1 Big Lesson
We tested the DeepSeek 70B, the 32B Distill, and the Coder-v2-lite. Here is what we found:
1. DeepSeek 70B: The Heavyweight
- The Reality: At FP16 precision, this model needs ~140GB of VRAM just to sit still.
- The Problem: Even with our combined 72GB VRAM, we hit an immediate Out-of-Memory (OOM) error.
- The Takeaway: For a 70B model to fit on a 48GB GPU, 4-bit (INT4) quantization isn’t an option; it’s a requirement. Unfortunately, the standard NVIDIA NIM container lacked a pre-compiled INT4 profile for our A6000.
2. DeepSeek-R1-Distill-Qwen-32B: The Middleweight
- The Reality: Default BF16 precision requires ~64GB.
- The Problem: Our 48GB A6000 fell just short. We tried to bridge the gap by using a second server (heterogeneous setup), but the container’s tensor parallelism requires identical (homogeneous) GPUs to split the load correctly.
3. DeepSeek-Coder-V2-Lite (16B): The “Lightweight”
- The Reality: Raw weights are only ~32GB.
- The Problem: We expected this to work, but once you add the KV Cache, activations, and framework overhead, it spiked past 48GB.
- The Takeaway: Without a pre-compiled quantized profile, even “small” models can crash on prosumer hardware.
🛠️ Selection Checklist: Parameters to Consider
Based on this research, here is my SRE Checklist for anyone selecting an AI module for their infrastructure:
- VRAM Footprint (Precision matters): Don’t just look at parameters (B). Calculate the precision.
- Formula: $Parameters \times Precision \ Bytes = Minimum \ VRAM$.
- Example: A 32B model at 16-bit (2 bytes) = 64GB.
- Quantization Availability: Does the container (like NVIDIA NIM) provide INT4 or INT8 profiles for your specific GPU architecture?
- GPU Homogeneity: If you are scaling across multiple GPUs, ensure they have identical VRAM capacity. Mixing a 48GB and a 24GB card will likely lead to “Connection reset by peer” errors in distributed frameworks.
- Operational Overhead: Always leave a 15–20% VRAM “buffer” for the KV Cache and system activations.
📝 Conclusion
The NVIDIA NIM container is powerful, but it isn’t always “plug-and-play” for non-data-center GPUs like the RTX A6000. My next diary entry will focus on bypassing pre-compiled profiles to create our own quantized versions.
The journey continues.
Leave a Reply