DeepSeek R1: The Reasoning Model That Re-Optimized the Entire Stack
Published:
Most “model comparisons” fixate on benchmarks. That misses the real story. DeepSeek was not a suprise, it was a necessary evolution in a resource-constrained (i.e., GPU constrained) environment.
DeepSeek R1, released January 20, 2025, is interesting because it’s not just a bigger LLM — it’s a different approach to how reasoning models are trained, accelerated, and deployed.
In my primer, I frame R1 against other modern reasoning families (including OpenAI-style reasoning models) and focus on what actually changes the game for builders: the stack.
Here’s the essence:
Training algorithm innovation: R1 spotlights Group Relative Policy Optimization (GRPO) as an alternative to PPO-style pipelines — aimed at faster convergence and improved stability in large-scale RL training. I give further details in my new AI textbook.
Performance engineering where it counts: Instead of living entirely inside CUDA abstractions, R1 discusses selectively dropping into NVIDIA PTX for critical kernels — squeezing throughput where training is actually bottlenecked.
Modularity over monoliths: R1 leans into Mixture-of-Experts (MoE) so specialized subnetworks can activate only when needed — reshaping the latency/cost curve while improving task specialization.
Practical deployment efficiency: R1 highlights more aggressive, task-aware quantization strategies to reduce memory and inference costs without giving away much capability.
The takeaway: the next decade of “reasoning breakthroughs” won’t come only from architectures. They’ll come from training dynamics + systems-level optimization + modularity—the pieces that turn raw capability into scalable, affordable deployment.
Read it here: DeepSeek_Primer.pdf
Final question:
If reasoning performance increasingly depends on the training algorithm and the hardware stack (not just parameters), are you evaluating models like products — or like end-to-end engineered systems?