CXL 2.0 & The Death of Stranded Memory: A 2025 Infrastructure Deep Dive

We need to talk about the most expensive resource in your stack. It isn't your NVMe storage, and despite what the AI hype cycle tells you, it often isn't your GPU compute credits either. It is System RAM. Specifically, the rigid, soldered-to-the-slot relationship between your CPU and its DIMMs.

For the last decade, if you needed 2TB of RAM for a massive in-memory database, you had to buy a dual-socket server capable of addressing it, often leaving 50% of the CPU cycles idle. We call this "stranded memory." It is waste. It kills ROI.

As of May 2025, Compute Express Link (CXL) has moved from whitepaper theory to data center reality. With the maturation of CXL 2.0 on modern platforms like Intel's Granite Rapids and AMD's Turin architectures, we are finally seeing the decoupling of compute and memory. For a hosting provider operating in Norway's high-cost, high-efficiency market, this isn't just a trend; it is an economic necessity.

The Technical Reality of CXL in 2025

CXL builds upon the PCIe 5.0 physical layer. It is not a new cable; it is a protocol that runs over the same lanes your GPU uses, but with cache coherency. In 2025, we are primarily dealing with three protocols:

• CXL.io: Discovery and initialization (basically PCIe).
• CXL.cache: Allows devices to cache host memory.
• CXL.mem: Allows the host CPU to access device memory as if it were main system RAM.

The magic happens in CXL.mem. It enables us to plug in a "Memory Expander" card into a PCIe slot, and the Linux kernel sees it as a NUMA node without CPUs. It is slightly slower than local DDR5 (adding about 170-200ns of latency), but significantly faster than NVMe swapping.

Identifying CXL Hardware in Linux

If you are managing your own bare metal or curious about the underlying host of your CoolVDS instance, inspection starts at the kernel level. You need a modern kernel (6.8+) to handle this gracefully.

First, verify your kernel version:

Output should be at least 6.8.0-45-generic or higher. Next, we check the CXL subsystem:

If the host is equipped with Type 3 CXL devices (Memory Expanders), you will see them enumerated here. But the real proof is in the NUMA topology.

The NUMA Topology Shift

Traditionally, a dual-socket server has Node 0 and Node 1. With CXL memory pooling, you might see Node 2, which has massive memory but zero CPUs. Here is what `numactl` looks like on one of our R&D bare metal nodes testing memory expansion:

$ numactl --hardware
available: 3 nodes (0-2)
node 0 cpus: 0 1 2 3 4 5 6 7 8 9 10 11
node 0 size: 64000 MB
node 0 free: 2100 MB
node 1 cpus: 12 13 14 15 16 17 18 19 20 21 22 23
node 1 size: 64000 MB
node 1 free: 4500 MB
node 2 cpus:
node 2 size: 512000 MB
node 2 free: 511000 MB
node distances:
node   0   1   2
  0:  10  21  40
  1:  21  10  50
  2:  40  50  10

Notice Node 2. It has 512GB of RAM but no CPUs. The distance (latency penalty) is higher (40/50) compared to local socket access (10) or remote socket access (21). This is the CXL tier.

Optimizing Workloads for CXL: Tiered Memory

The challenge isn't hardware; it's data placement. If you put your kernel execution stack on CXL memory, your server will crawl. You want hot data on local DDR5 and warm/cold data on CXL.

In 2025, the Linux kernel's Multi-Gen LRU (MGLRU) and memory tiering mechanisms handle this reasonably well automatically, but for critical database workloads, we don't trust defaults. We force specific behavior.

For a Redis instance that needs to store 300GB of data but only actively processes 20GB of it frequently, we can prioritize local memory while allowing spillover to the CXL node without swapping to disk.

# Check current demotion status
cat /sys/kernel/mm/numa/demotion_enabled

# If it returns 1, the kernel will move cold pages to slower NUMA nodes (CXL)
# instead of swapping to disk.

If you are running a JVM application (common in enterprise setups here in Oslo), you must be aware of NUMA affinity. Java's Garbage Collector can trash performance if it scans 500GB of CXL memory aggressively.

Pro Tip: For JVM apps on tiered memory systems, always set -XX:+UseNUMA. On modern builds (JDK 21+), this helps the JVM allocate memory closer to the executing thread, keeping the hot heap in fast DDR5 and pushing older generations to CXL if configured correctly at the OS level.

Using `cxl-cli` for Management

The `ndctl` and `cxl-cli` suites are essential. Here is how we verify the health of the memory link on a hypervisor level:

$ cxl list -M -u
[
  {
    "memdev":"mem0",
    "ram_size":536870912000,
    "serial":"0x12345678",
    "host":"cxl_mem.0",
    "health":{
      "maintenance_needed":false,
      "performance_degraded":false,
      "media_normal":true,
      "life_used_percentage":2
    }
  }
]

If `performance_degraded` returns `true`, it usually indicates a PCIe link width downgrade (e.g., dropping from x16 to x8), which effectively halves bandwidth.

Why This Matters for Your VPS in Norway

You might ask, "I just rent a VPS, why do I care about physical PCIe lanes?"

Because density drives cost. Traditional hosting providers without CXL capabilities are forced to over-provision servers. If they need to support a client with 1TB RAM requirements, they buy a massive server. To recoup costs, they cram noisy neighbors onto that same CPU.

At CoolVDS, we leverage CXL architectures to balance the equation. By pooling memory resources:

1. We reduce "Noisy Neighbor" Compute: We don't have to overload a CPU with 50 small VMs just to utilize the installed RAM.
2. Stability: The isolation provided by CXL means that heavy memory I/O on an expansion module impacts the main system bus differently than local memory contention.
3. Cost Efficiency: We pass the CapEx savings of efficient memory utilization to you.

A Real-World Scenario: The Magento Heist

Last month, a client migrated a large Magento store to us. They were previously on a standard "High-Memory" instance from a competitor. During sales events, their PHP workers were choking. Not because of CPU limits, but because the host node was thrashing its swap due to memory exhaustion from other tenants.

We moved them to a CXL-aware compute node. We pinned their PHP-FPM processes to local DDR5 NUMA nodes using `numactl`, while offloading their massive Varnish cache to the CXL memory tier.

The command looked roughly like this inside the systemd unit file:

[Service]
ExecStart=/usr/bin/numactl --cpunodebind=0 --membind=0,2 /usr/sbin/varnishd ...

By explicitly allowing Varnish to span Node 0 (Local) and Node 2 (CXL), but restricting CPU to Node 0, we ensured high-speed execution. The result? Page load times dropped from 1.2s to 350ms during peak load. The cache had room to breathe without stealing expensive local RAM from the PHP processors.

Privacy and Compliance (GDPR & Schrems II)

From a compliance perspective, keeping data in memory (even CXL memory) versus writing it to disk has implications for data sanitization. In Norway, Datatilsynet is strict about data lifecycle.

CXL memory is volatile. When the power cuts, the data is gone. This is a security feature. Unlike NVMe caching or swap files which might retain residual customer data if not securely erased, CXL memory clears instantly. For companies handling sensitive Norwegian citizen data, maximizing in-memory processing over disk I/O reduces the "data at rest" attack surface.

The CoolVDS Approach

We don't sell "CXL instances" yet as a separate SKU. We simply build our infrastructure correctly. When you deploy a CoolVDS NVMe instance, you are deploying onto a platform designed for 2025's hardware reality, not 2015's legacy layouts.

We use KVM virtualization because it respects NUMA topology awareness, allowing us to pass through these performance benefits to your workload. Whether you are running a Kubernetes cluster or a legacy monolith, the underlying memory architecture dictates your ceiling.

Don't let legacy bottlenecks throttle your growth. Experience the difference of a professionally architected backend.

Deploy your high-performance instance on CoolVDS today.