Latest insights, updates, and stories on AI & compute.
Reinforcement learning fine-tuning (RFT) is powerful — but let’s face it: it used to be a pain to run. Dual networks, huge memory needs, tons of config files... That’s why we built RUNRL JOB — the easiest way to run RFT workloads like GRPO directly on HPC-AI.COM. No complicated setup. Just pick your model, launch your job, and go.
Reinforcement learning (RL) has transformed how we fine‑tune language models. Traditional approaches like Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO) use a ‘critic’ value network—doubling model size, memory requirements, and complexity. Meanwhile, human‑alignment methods like Direct Preference Optimization (DPO) optimize for preference, not reasoning.
In 2025, open-source AI is exploding. Meta’s LLaMA 4, the latest in the LLaMA series, is setting new benchmarks for reasoning, multilingual fluency, and tool use. From chatbots to copilots, it's already powering the next wave of AI apps. However, running LLaMA 4 — or any large model at scale often requires time-consuming setup, infrastructure engineering, and DevOps.
We're thrilled to introduce Open-Sora 2.0, a cutting-edge open-source video generation model trained with just $200,000 — delivering 11B parameter performance on par with leading closed-source models like HunyuanVideo and Step-Video (30B). And now, you can fine-tune or run inference with Open-Sora 2.0 instantly — on the HPC-AI.COM GPU cloud, with no contracts, global coverage, and prices starting at just $1.99/GPU hour.
DeepSeek V3/R1 is a hit around the world, with solutions and API services based on the original model becoming widely available, leading to a race to the bottom in pricing and free offerings. How can we stand on the shoulders of the giant and leverage post-training with domain-specific data to build high-quality private models at low cost, enhancing business competitiveness and value?
DeepSeek-R1 is the most popular AI model nowadays, attracting global attention for its impressive reasoning capabilities. It is an open-source LLM featuring a full CoT (Chain-of-Thought) approach for human-like inference and an MoE design that enables dynamic resource allocation to optimize efficiency. It substantially outperforms other closed-source models in a wide range of tasks including coding, creative writing, and mathematics.