intel optane for AI workloads

# Intel Optane for AI Workloads: A Missed Opportunity That Still Shines **TL;DR:** Intel’s discontinued Optane technology—a persistent, byte‑addressable memory tier between DRAM and SSD—is finding a second life in AI workloads, enabling a user to run a 1 trillion parameter model locally at ~4 tokens/second using six cheap second‑hand Optane persistent memory modules. ## The Rise and Fall of Optane Optane was built on 3D XPoint, a joint venture between Intel and Micron. It was designed to fill the gap between fast, expensive DRAM and slow, cheap NAND SSDs. Optane offered larger, cheaper, byte‑addressable storage that was faster than any SSD but slower than DRAM—and crucially, it was persistent. Unfortunately, Optane arrived too early. The final blow was a collapse in memory prices that made Optane completely unprofitable. As one Reddit comment put it: “3D XPoint … just came a little bit too early. And the nail in the coffin was the memory price crash that made Optane completely unprofitable.” Intel and Micron eventually parted ways, and Intel discontinued the Optane product line. Today, you can still buy small Optane cache drives (e.g., 32 GB M.2 modules) for around $40 each on eBay. ## A Surprising AI Use Case Despite its demise, Optane has proven to be an unexpected gem for running large language models (LLMs) locally. A Reddit user, AP Frisco, built a system using six used Intel Optane persistent memory modules (totaling 768 GB) in an Intel Xeon workstation, configured in “Memory Mode” with DDR4 acting as a cache. On this setup, they ran a 1 trillion parameter Kimmy K2.5 model at roughly 4 tokens per second. The key insight: Optane’s latency is far lower than NVMe (even PCIe Gen 4 or 5 SSDs), though still about 2–3× higher than DRAM. This makes it possible to keep a massive model in “memory” without resorting to slow storage. In the specific workflow, the bulk of the model resided in Optane/CPU memory, while critical tensors were streamed to a 12 GB RTX 3060 GPU using Lambda’s memory offloading feature. ### Performance Numbers - **Model size:** 1 trillion parameters - **Inference speed:** ~4 tokens/second - **Optane memory:** 6 × 128 GB (768 GB total), configured as memory with DDR4 cache - **GPU:** Single RTX 3060 (12 GB VRAM) - **Cost:** ~$20 per module (second‑hand) The user noted that speed could likely be much higher with better compute; the system was severely limited by GPU compute, not by Optane bandwidth. In fact, one commentator pointed out: “I bet it could be a lot faster. They didn’t even mention compute capacity.” ## Why Optane Matters for AI The fundamental problem with running large models locally is memory capacity. Even high‑end consumer systems top out at 128–256 GB of DRAM. Beyond that, you must fall back to SSDs, which suffer from high latency and poor random access performance. Optane sits in the sweet spot: it’s cheaper per gigabyte than DRAM, faster than any SSD, and persistent. In the words of Linus (from the tech community): “Rest in peace, Optane. You were too great, and too early.” He went on to note that some PCIe Gen 3 Optane drives *still* outperform Gen 4 and even Gen 5 SSDs in real‑world IOPS and latency—not in sequential file copy benchmarks, but in the random access patterns that matter for AI and database workloads. “AI is literally the perfect use case. We’ve been searching for the answer: how do we run these huge models without putting them on much slower storage?” The ideal solution is an Optane‑cached Optane tier. ## What Optane Offers Today Even though the technology is discontinued, the hardware remains available. Several tech enthusiasts and labs have accumulated significant Optane inventory: - **15.7 TB** of Optane in one storage rack - **12 × 512 GB** Optane DC persistent memory modules (6 TB) sitting in a forgotten server in a warehouse - **452 individual Optane drives** owned by one person That server—an Intel 1U system loaded with 6 TB of Optane DIMMs—could be repurposed for AI experiments. The team discussed dropping it into a lab with a large GPU to see what it could do. Combined with another 6 TB of PCIe NVMe Optane, the total available Optane in their possession reached roughly **12 TB**. ## The Cache Potential Smaller Optane modules (e.g., 32 GB M.2) are still cheap and can serve as ultra‑low‑latency caches for hard drives. Even a relatively small cache dramatically improves performance for mechanical disks, especially when used with third‑party caching tools (Intel’s own tools may be deprecated). One enthusiast noted that a $40 Optane M.2 module could make a huge difference for a large spinning disk. ## A Fond Farewell The conversation ended with a mix of nostalgia and excitement: “I love you, Optane. I wish you could come back, but it’s never happening.” Yet the community is already proving that Optane, even in its discontinued state, has a vibrant second life powering AI workloads that its creators never fully anticipated. --- **Source:** [YouTube: intel optane for AI workloads](https://www.youtube.com/watch?v=-obyhc50mCE)