My Journal: Challenges of Training Large Language Models on Consumer GPUs: My RTX 4090 Journal

In this article, I'll share my personal journal of training Large Language Models (LLMs) on consumer-grade GPUs, specifically a setup of six NVIDIA RTX 4090s. This journey has been filled with obstacles, learnings, and breakthroughs that I hope will provide valuable insights for others treading a similar

DAY 1: Excitement and First Attempts

I began my journey with great enthusiasm. My setup of six RTX 4090s, each with 24GB of GDDR6X memory, totaling 144GB of VRAM, seemed like a powerhouse capable of handling substantial LLM training tasks. However, I quickly realized that even this impressive array would face challenges with the largest models.

Week 1: Grappling with Memory Limitations and Exploring GPU DifferencesThroughout the first week

I found myself constantly battling against memory constraints, even with my impressive setup of six RTX 4090s. To better understand the challenges I was facing, let's explore the key differences between consumer-grade GPUs like the RTX 4090 and enterprise-grade options like the NVIDIA A100 or H100:

1. Memory Capacity and Bandwidth:

• RTX 4090: 24GB GDDR6X memory with 1 TB/s bandwidth
• A100: Up to 80GB HBM2e memory with 2 TB/s (40GB) or 3.35 TB/s (80GB) bandwidth
• H100: 80GB HBM3 memory with 3.35 TB/s bandwidth

The limited memory on the RTX 4090 quickly became a bottleneck, especially when loading large language models and datasets.

2. Compute Performance:

• RTX 4090: Up to 82.58 TFLOPS for FP32 operations
• A100: Up to 19.5 TFLOPS for FP32, but excels in AI-specific tasks with up to 624 TFLOPS for FP16 Tensor Core operations
• H100: Up to 51.22 TFLOPS for FP32, and up to 1000 TFLOPS for FP16 Tensor Core operations

While the RTX 4090 offers impressive raw performance, the enterprise GPUs are specifically optimized for AI workloads, providing significantly higher performance for the operations most common in LLM training.

3. Specialized Features:

• RTX 4090: Primarily designed for gaming, with features like ray tracing and DLSS
• A100/H100: Include features like Multi-Instance GPU (MIG) technology, NVLink for high-speed multi-GPU connectivity, and are highly optimized for AI workloads

The lack of enterprise-focused features on the RTX 4090 became apparent as I tried to scale my training across multiple GPUs.

4. Memory Management and Error Handling:Consumer GPUs: More prone to CUDA out of memory errors due to less sophisticated memory managementEnterprise GPUs: Better driver and software optimizations for handling large AI workloads, potentially reducing the frequency of out-of-memory errors

I frequently encountered CUDA out of memory errors when attempting to load large models or use substantial batch sizes, highlighting the limitations of consumer GPU memory management.

5. Cooling and Power Efficiency:RTX 4090: Designed for intermittent, high-performance use in desktop environmentsA100/H100: Engineered for continuous operation in data center environments

As I pushed my GPUs to their limits, I noticed thermal throttling and power consumption issues that wouldn't typically be a concern with enterprise-grade hardware.

6. Software and Driver Support:Consumer GPUs: Primarily optimized for gaming and general-purpose computingEnterprise GPUs: Extensive optimizations for AI and HPC workloads, with better support for AI frameworks and libraries

I found that some AI libraries and frameworks weren't fully optimized for my consumer GPUs, leading to suboptimal performance in certain scenarios.Throughout the week, these differences manifested in several key challenges:

1. Model Size Constraints: I struggled to load and train models larger than 7-13 billion parameters, depending on the specific architecture and training approach.
2. Batch Size Restrictions: I was forced to use smaller batch sizes than ideal, potentially impacting training efficiency and convergence.
3. GPU Memory Allocation: A large portion of the GPU 0 memory was quickly consumed by CUDA operations and the model itself, leaving limited space for training data and intermediate computations.
4. Scaling Complexities: Efficiently managing workloads across six GPUs required careful optimization and introduced additional complexity to the training process.
5. CPU Offloading Attempts: In an effort to alleviate GPU memory pressure, I tried offloading parts of the model to CPU. This led to an unexpected bottleneck: system RAM exhaustion, causing the entire process to be terminated by SIGTERM.

Week 2: Exploring Solutions

Determined to make progress, I began researching and implementing various techniques:

1. Gradient Checkpointing: This allowed me to trade some computation speed for reduced memory usage, enabling work with larger models.
2. Mixed Precision Training: Implementing 16-bit floating-point precision for certain operations helped manage memory more efficiently across my GPU array.
3. Model Parallelism: I experimented with splitting models across multiple GPUs, which proved crucial for handling larger architectures.

Week 3: Refinement and Reflection

As I refined my approach, I reflected on the challenges specific to consumer hardware:

1. Memory Capacity: While 144GB of VRAM is substantial, it still pales in comparison to high-end enterprise solutions, especially when dealing with the largest LLMs.
2. CPU RAM Limitations: I discovered that my system's RAM became a bottleneck when attempting to offload from GPUs, highlighting the need for a balanced hardware configuration.
3. Scaling Complexities: Efficiently managing workloads across six GPUs required careful optimization and introduced additional complexity to the training process.

Week 4: Breakthroughs and Advanced Techniques

In my final week of intense experimentation, I made significant breakthroughs:

1. Streaming Implementation: I successfully implemented a streaming approach, allowing me to process larger models by handling them in smaller chunks. This was a game-changer for working with models that exceeded both GPU and CPU memory capacities.
2. GPU and CPU Offloading: I managed to use a combination of GPU and CPU offloading, although I had to carefully manage this due to CPU RAM limitations.
3. Dynamic Batching: Adjusting batch sizes based on sequence length helped maximize GPU utilization while staying within memory limits.
4. Efficient Data Loading: Optimizing my data pipeline reduced overall memory pressure, allowing for smoother training runs across all six GPUs.

My conclusions:

Lessons LearnedMy month-long journey of training LLMs on six RTX 4090s was both challenging and enlightening. While I couldn't match the capabilities of enterprise-grade hardware, I learned to push consumer GPU technology to its limits:

1. Multi-GPU Scaling: Effectively utilizing multiple GPUs is crucial but introduces complexity in terms of data and model parallelism.
2. Balanced Hardware Configuration: CPU capabilities and system RAM are as important as GPU specs when working with large models.
3. Advanced Techniques are Essential: Streaming, offloading, and dynamic memory management techniques are not just optimizations but necessities for training large models on consumer hardware.
4. Software Optimization: Utilizing frameworks and libraries optimized for large model training on consumer hardware can significantly improve performance and capabilities.
5. Flexibility and Creativity: Successfully training LLMs on consumer hardware requires constantly adapting to hardware limitations and thinking creatively about resource utilization.

For researchers and enthusiasts working with limited resources, my experience shows that meaningful work on LLMs is possible with consumer GPUs, even when pushing into territory typically reserved for enterprise hardware. It requires creativity, patience, and a willingness to work within and around hardware constraints.The field of AI is rapidly evolving, and today's limitations may be tomorrow's solved problems. By sharing my journal, I hope to contribute to the collective knowledge that will drive innovation and make AI more accessible to all, even those working with consumer-grade hardware.