The NVIDIA RTX 4000 Small Form Factor (SFF) Ada GPU has emerged as a compelling option for those looking to run Large Language Models (LLMs), like Llama 3.2, Mistral and Qwen2.5, in compact and power-efficient systems. While it may not grab headlines like its consumer-oriented RTX 4090 sibling, this professional-grade card offers a unique blend of performance, efficiency, and features that make it particularly well-suited for LLM workloads.

Specifications and Design

The RTX 4000 SFF Ada is built on NVIDIA’s latest Ada Lovelace architecture, offering:

• 6,144 CUDA cores
• 192 Tensor cores
• 48 RT cores
• 20GB GDDR6 memory with ECC
• 280 GB/s memory bandwidth
• 70W TDP
• PCIe 4.0 x16 interface
• Compact dual-slot design

The card’s 20GB of VRAM is a standout feature, providing enough memory for running moderately sized quantized LLMs. This generous VRAM allocation, combined with the Ada architecture’s improved efficiency, positions the 4000 SFF ADA  as an intriguing option for working with open weight language models.

LLM Performance

In our benchmarks focused on LLM inference, the RTX 4000 SFF Ada demonstrates impressive capabilities, especially considering its compact form factor and low power consumption:

For context, these results show the 4000 SFF Ada achieving about 46-52% of the performance of an RTX 3090 across various model sizes and quantization levels. This is remarkable considering the 4000’s significantly lower power envelope and smaller physical footprint.

llama.cpp running Qwen2.5 32B IQ4_XS on RTX 4000 SFF Ada

The 32B IQ4_XS benchmark, utilizing the Qwen2.5-32B-Instruct-IQ4_XS model (currently one of the best open-weights LLM), showcases the 4000’s ability to handle larger, more complex models while still maintaining respectable performance. At 16.93 tokens/second, it offers a fluid experience for many LLM based applications.

Real world application

Instead of just throwing benchmarks around, we wanted to see the NVIDIA RTX 4000 SFF Ada in action. So, we built a compact, completely private LLM inference server powered by this card and an OptiPlex small form factor PC. Our goal: Alexa-like responses, but without the “Alexa” part (no cloud, all local processing). A Willow device (ESP32-S3-BOX) captures your voice, the RTX 4000 SFF Ada running Llama 3.2 8B does the thinking, and a speaker delivers the answers.

OptiPlex SFF Desktop with RTX 4000 SFF Ada

Willow ESP32-S3-BOX device

The result? Snappy performance, surprisingly natural conversations, and all within a system that fits comfortably on a bookshelf. Who needs bulky servers?

Limitations

While the NVIDIA RTX 4000 SFF Ada excels as a compact and power-efficient solution for running moderately sized LLMs, it’s essential to acknowledge its limitations, especially when compared to the reigning champion of locally hosted LLMs, the GeForce RTX 3090.

The 4000’s lower memory bandwidth (280 GB/s vs. the 3090’s 936 GB/s) will generate 50% less tokens per second. Furthermore, the 4000 currently commands a price premium in the used market, hovering around $1100, while a used RTX 3090 can be found for roughly $650. Heck, you could even snag a pair of used RTX 4060 Ti 16GB (32GB total) cards and have yourself a pretty potent LLM setup for under $800.

This price difference, coupled with the 3090’s raw performance advantage, makes the consumer-grade card a compelling alternative, provided its power consumption and physical size are manageable within the user’s deployment scenario. For those prioritizing frugality and top-end performance above all else, the 3090 remains a strong contender, even against newer professional-grade offerings.

Power Efficiency and Thermal Performance

One of the most impressive aspects of the RTX 4000 SFF Ada is its power efficiency. Operating within a 70W TDP, it delivers substantial LLM performance while consuming only a fraction of the power required by high-end consumer GPUs. This efficiency translates to lower operating costs and reduced cooling requirements, making it an excellent choice for 24/7 operation or deployment in space-constrained environments.

Thermal performance is equally noteworthy, with the card maintaining temperatures around or below 70°C under sustained load. The well-designed cooling solution allows for consistent performance without thermal throttling, even in compact systems with limited airflow.

Professional Features

As a member of NVIDIA’s professional GPU lineup, the RTX 4000 SFF Ada includes several features that set it apart from consumer cards:

• Error-Correcting Code (ECC) memory for enhanced reliability
• Optimized drivers for professional applications
• Support for NVIDIA GPU Direct technology
• Four Mini DisplayPort 1.4 outputs

These professional-grade features, while not directly impacting LLM performance, contribute to the overall stability and versatility of systems built around the 4000.

Use Cases and Positioning

The RTX 4000 SFF Ada excels in scenarios where power efficiency, compact size, and moderate LLM performance are prioritized. It’s particularly well-suited for:

• Edge AI deployments requiring local LLM inference
• Compact workstations for AI researchers and developers
• Home lab setups for experimenting with AI and machine learning
• Small-scale production environments running multiple AI services

While it may not match the raw performance of high-end consumer GPUs like the RTX 4090, the 4000 SFF Ada offers a balanced approach that makes it the most powerful SFF GPU option for LLM workloads, especially when considering its power efficiency and form factor.

Conclusion

The NVIDIA RTX 4000 SFF Ada represents a significant step forward in bringing powerful LLM capabilities to compact, energy-efficient systems. Its ability to run moderately sized language models at respectable speeds, all while consuming just 70W of power, makes it a standout option in the professional GPU market.

For users prioritizing a balance of performance, efficiency, and size in their LLM workflows, the RTX 4000 SFF Ada emerges as a top choice. It may not be the fastest option available, but its unique combination of features and capabilities makes it arguably the most well-rounded SFF GPU for LLM applications currently on the market.

As the field of AI and machine learning continues to evolve, GPUs like the 4000 SFF Ada demonstrate that impressive AI performance need not be limited to power-hungry, oversized cards. For many users, this compact powerhouse may well represent the sweet spot in the current landscape of AI-focused GPUs.