Intel just dropped its Xeon 600 series processors and honestly the timing couldn’t be more interesting. After nearly three years away from the workstation market, they’re back with Granite Rapids architecture packing up to 86 cores and support for a frankly ridiculous 4TB of DDR5 memory. But what really caught my attention is how these chips are built specifically with AI workloads in mind.
As someone who’s been testing and reviewing workstation hardware for AI development and content creation. I can tell you that the industry has been waiting for this. The previous Sapphire Rapids generation felt dated almost immediately and AMD’s Threadripper Pro has been dominating the conversation. Now Intel’s finally responding with something worth discussing.
Why Granite Rapids Actually Matters for AI Work
Look, we’ve all heard processor launch hype before. But the Xeon 600 series brings something genuinely useful to the table upgraded AMX accelerators with new FP16 support. If you’re running local AI models, doing machine learning development or just trying to keep your creative workflows running smoothly with AI tools. This hardware acceleration makes a real difference.
I’ve spent the past year working with various AI tools for content creation from running local LLMs for research to generating images with Stable Diffusion. The bottleneck is almost always either memory or inference speed. Intel seems to have recognized this reality.
The flagship Xeon 698X sits at the top with 86 cores, 336MB of L3 cache and a 4.8 GHz turbo boost. Intel claims 61% better multi-threaded performance over the previous generation. Which is a substantial jump. But the real story is how they’ve optimized these Redwood Cove cores for the kind of work people actually do in 2026. Running LLMs locally, processing Stable Diffusion generations and handling AI inference without constantly relying on cloud services.
The architecture doubles the L1 instruction cache to 64KB and adds AVX-512-FP16 instructions. That might sound technical, but it translates to noticeably faster performance when you’re running models like Llama or custom fine-tuned networks on your local machine. In practical terms, this means less waiting around for your AI assistant to generate responses or your image model to render outputs.
Memory: Intel’s Secret Weapon
Here’s where things get interesting and where Intel might have actually nailed it. The Xeon 600 supports up to 4TB of RAM – literally double what AMD’s Threadripper Pro can handle. The top-tier models even support MRDIMMs running at 8,000 MT/s, delivering about 844 GB/s of memory bandwidth.
Check this article – Is 1TB RAM Possible? Here’s How Gigabyte Just Made It Real
Why does this matter? Try running multiple AI models simultaneously or working with mixture-of-experts architectures that need tons of memory. Suddenly that extra capacity becomes incredibly valuable. When you’re loading a 70B parameter model with long context windows. You need every gigabyte you can get.
From my experience building AI workstations, memory has become the limiting factor more often than CPU power. You can have all the cores in the world, but if you can’t keep your models in RAM. You’re stuck swapping to disk and watching your productivity crater.
Plus, all models come with 128 PCIe 5.0 lanes and CXL 2.0 support. If you’re building a multi-GPU setup for AI training or high-end rendering. You won’t hit bottlenecks trying to feed those GPUs data. I’ve seen too many builds where people drop $10K on GPUs only to have their PCIe lanes maxed out.
Intel vs AMD: The Real Comparison
Intel’s marketing materials conveniently avoided direct AMD comparisons. Which tells you something right there. When pressed during the briefing, Intel’s Jonathan Patton gave the classic better performance per dollar line. Let’s look at what that actually means in real-world terms.
The 64-core Xeon 696X costs $5,599, undercutting AMD’s equivalent Threadripper Pro by around $2,000. That’s significant savings enough to buy additional RAM or a better GPU. However, AMD’s flagship 9995WX pushes 96 cores and hits 5.4 GHz turbo speeds 10 more cores than Intel’s best chip and higher clock speeds to boot.
For AI-specific work, it gets complicated. AMD’s 5nm architecture delivers 96 cores at just 350W and their AVX-512 implementation handles AI tasks quite well. Recent benchmarks show AMD EPYC chips (Threadripper’s datacenter cousins) delivering about 1.23x better performance per dollar on Llama2 inference compared to Intel’s AMX-enabled Xeons.
But Intel has that memory advantage and dedicated AMX hardware for AI inference that AMD simply doesn’t offer yet. Having tested both platforms extensively, I’d say this: if your workflow involves massive datasets or running multiple AI instances simultaneously. Intel’s memory capacity edge is hard to ignore. If you need raw parallel processing power and efficiency, AMD’s core count advantage matters more.
Depending on your specific workflow whether you prioritize raw core count or AI-optimized silicon either platform could make sense. There’s no universal winner here, despite what the marketing wants you to believe.
The Market Reality Check
Systems from Dell, HP, Lenovo, Supermicro and Puget Systems should hit shelves in late March. You’ll also see W890 motherboards from Asus, Gigabyte, and Supermicro. Intel’s offering five retail boxed processors (654, 658X, 676X, 678X, and 696X), with six X-series models featuring unlocked overclocking.
But here’s the uncomfortable truth that Intel’s press materials glossed over: the launch arrives during what everyone’s calling a memory winter. DDR5 RDIMM prices have tripled since late 2025 and analysts expect another 40% increase in Q1 2026. A modest 8x32GB kit now runs over $4,000, up from roughly $1,500 just six months ago.
I’ve been tracking memory prices closely because it directly impacts the builds I recommend to clients and readers. If you’re speccing out a full 4TB system, you’re looking at $70,000+ just for RAM. That’s not a typo. The processor might cost $7,699 but the memory to max it out costs ten times more.
Meanwhile, Intel’s datacenter Xeon capacity is sold out through 2026. which is why they’ve deprioritized desktop and mobile chip production. So availability might be spotty initially and we might see price gouging from resellers. Be cautious about overpaying in the first few weeks.
Who Should Actually Buy These Intel Xeon 600?
After covering enterprise hardware for several years and building dozens of workstations for various use cases, here’s my straightforward assessment:
If you’re doing serious AI development work, running local inference regularly or need massive memory capacity for LLM workflows. the Xeon 600 series genuinely delivers value. The combination of high core counts with purpose-built AI acceleration makes these processors particularly compelling for professionals who’ve moved beyond hobby-level AI experimentation.
The lineup ranges from $499 to $7,699, so there are entry points for different budgets though memory costs might still blow your budget regardless. For workstation builders who prioritize AI performance and need maximum memory capacity. Intel’s offering something that neither previous-gen Intel chips nor current AMD alternatives can match.
However, I wouldn’t recommend rushing out to buy day one. Wait for independent benchmarks (including ours, which we’ll publish once review units arrive). Let the early adopters work through any platform teething issues. And most importantly watch those memory prices they might stabilize in Q2 2026, saving you thousands.
Just be prepared for sticker shock when you start configuring your build. The processors themselves are reasonably priced for what they deliver. It’s everything else that’ll hurt your wallet. I’ve learned this lesson the hard way with previous generation launches, and I’m sharing that experience so you don’t make the same mistakes.
Bottom line: The Xeon 600 series represents Intel’s strongest workstation offering in years, particularly for AI workloads. But buy smart, not fast.
Disclosure: This article is based on Intel’s official briefing materials and publicly available specifications. TechGlimmer has not yet received review units for independent testing. We’ll update this coverage once hands-on benchmarks are available.
