Spark Hardware Delivers 40 Tokens/Sec for DeepSeek

Dual Spark Setup Achieves Impressive Inference Speed

AI researcher Teknium has successfully demonstrated a powerful dual-Spark hardware configuration that delivers approximately 40 tokens per second when running DeepSeek V4 Flash (0731 abliterated version). By connecting two Spark units with a single cable, this setup enables high-performance local inference without relying on cloud services. The achievement represents a significant milestone for developers and researchers seeking powerful on-premises AI capabilities. This configuration runs without dSpark optimization, suggesting even better performance might be possible with further tuning. The 40 tok/s throughput makes it viable for real-time applications, chatbots, and interactive AI experiences while maintaining complete data privacy and control over the inference pipeline.

Understanding the DeepSeek V4 Flash Model

DeepSeek V4 Flash (version 0731) in its abliterated form represents a completely uncensored language model capable of generating responses without content filtering or safety guardrails. The abliteration process removes built-in restrictions, allowing the model to respond to any prompt without refusing based on ethical guidelines. This makes it particularly valuable for researchers, red-teaming exercises, and applications where content filtering interferes with legitimate use cases. The Flash variant is optimized for speed, trading some capability for faster inference times. Running such models locally on dedicated hardware ensures complete privacy, as no data leaves the user's infrastructure. This combination of speed, capability, and privacy makes DeepSeek V4 Flash an attractive option for sensitive applications.

The Spark Hardware Ecosystem for AI

Spark represents a new category of dedicated AI inference hardware designed specifically for running large language models locally. Unlike traditional GPU setups, Spark units are purpose-built for transformer architectures and optimized for the memory bandwidth requirements of modern LLMs. The ability to daisy-chain multiple units addresses one of the biggest challenges in local AI deployment: memory capacity. Teknium's mention of hoping for 512GB in the Spark2 generation highlights the ongoing memory limitations when running large models. Current configurations already deliver competitive performance, but doubling or tripling memory capacity would enable running even larger models with billions more parameters. This hardware approach democratizes access to powerful AI by making local deployment more accessible than ever before.

Privacy and Uncensored AI Inference Benefits

Running completely private, uncensored AI inference offers distinct advantages for various use cases. Researchers can explore model behavior without external oversight or data collection. Companies handling sensitive information can process data without exposing it to third-party API providers. Developers working on creative applications avoid arbitrary content restrictions that might flag legitimate scenarios. The combination of privacy and uncensored output creates possibilities that cloud-based API services cannot match due to legal, ethical, or business constraints. However, this freedom also requires responsible usage and understanding of potential risks. Organizations deploying such systems must implement their own governance frameworks. The technical capability to run these models locally represents an important step toward AI sovereignty and data independence.

Future Hardware Expectations: Spark2 and Beyond

Teknium's comment about hoping for 512GB in the Spark2 generation reveals both current limitations and future possibilities. Modern large language models increasingly require massive memory capacity to run efficiently, especially when serving multiple concurrent requests or running models with hundreds of billions of parameters. A 512GB configuration would represent a substantial leap, potentially enabling GPT-4 class models to run entirely on local hardware. The evolution of specialized AI inference hardware continues to accelerate, with each generation bringing better performance-per-watt, higher memory bandwidth, and improved thermal management. As these systems mature, the gap between cloud-based and local inference capabilities narrows. For many organizations, the tipping point where local deployment becomes more economical than API costs is rapidly approaching.