Kove

What if your real performance constraint wasn’t memory, but mindset? Kove:SDM™ turns stranded DRAM into a shared, high-performance resource across production environments. Radically simple to adopt, it requires no hardware changes and no app rewrites. Just software and instant memory scale, ready to deploy today. Learn more here: https://bit.ly/4bbwnVY

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Kove is proud to join Red Hat in the Partner Pod. Visit the Red Hat booth for an interactive look at how we’re building the future of open innovation together.

Stop by to explore:
🚀 Success stories
🛠️ Joint solution deep dives
🎥 Exclusive video content

Where: Red Hat Booth (Hall 2, Stand 2F30)

Increase your inference throughput without purchasing more GPUs.

Modern inference engines (vLLM, SGLang, TensorRT-LLM) depend on KV cache reuse to avoid recomputation and keep latency low. When KV cache outgrows system DRAM, data is evicted or recomputed, wasting GPU cycles and driving up cost.

The problem: falling out of DRAM is expensive.
Fast, predictable memory access becomes slow and unpredictable and has the potential for recompute - killing efficiency and increasing costs. Even the fastest NVMe tiers are 100–1,000x slower. Recompute may be worse still.

Kove:SDM™ removes that constraint.
• Sustain DRAM-class latency beyond local memory limits
• Support 5x larger CPU-side KV caches without tiering to storage or recomputation
• Keep GPUs productive instead of stalled on memory
• Scale inference memory independently of server DRAM

When KV cache stays resident, inference stays fast and GPUs stay efficient.
See how software-defined memory changes inference economics: https://bit.ly/3Oe3dxV

Here’s why this news matters. It signals that, as we’ve been saying for years, memory is no longer a background component of AI infrastructure, it’s a national, economic priority.

But the reality for enterprise teams shipping AI right now is, fab timelines don’t protect you from memory economics in 2026 and 2027. If your plan is to wait around for DRAM markets to “normalize,” you’ll only fall further behind.

The organizations that engineer around scarcity now with Kove:SDM™ are:
- Pooling memory across servers
- Reducing their stranded capacity
- Scaling workloads without scaling DRAM purchases at peak pricing

At a time when supply is getting the investment, it’s efficiency that needs the innovation. If memory is becoming your AI bottleneck, we want to know: what’s your plan to solve for it?

Read the full article from The Street here: https://bit.ly/3NMEXCR

On average, only 30–40% of enterprise DRAM is actually utilized.
The rest sits idle, stranded by server-bound architectures.

At the same time, DRAM spot prices have surged, and supply constraints are expected to persist into 2027, as manufacturers prioritize HBM for AI accelerators. For many organizations, this creates a false choice: pay more for memory, slow AI initiatives, or wait.

The better option isn’t buying more memory.
It’s using the memory you already own far more effectively.

Kove:SDM™ transforms stranded DRAM into a shared, high-performance resource across the data center. By decoupling memory from individual servers, organizations can increase utilization, reduce overprovisioning, and scale memory-intensive workloads without buying additional DRAM at inflated prices.

The result is materially better economics:
- Higher memory utilization from existing assets
- Fewer servers required to support growing workloads
- Lower power, cooling, and total cost of ownership
- Faster time to value for AI and data-intensive initiatives

Organizations that rethink memory architecture now are better positioned to operate through continued price volatility — and to emerge more efficient on the other side.

Is your infrastructure team planning for the DRAM shortage?
We’d love to hear how you’re approaching memory constraints. Drop a comment or send us a DM and let’s maximize your memory architecture.

The Enterprise Neurosystem’s latest article highlights an important milestone: the ratification of the Natural Language Interaction Protocol (NLIP), a step toward standardizing how AI agents communicate, reason, and act across systems.

Why does this matter for enterprise AI?
• AI agents only deliver value if they can operate at scale, in real time
• That scale is increasingly constrained by memory, not compute
• Standards like NLIP accelerate adoption, but they also expose infrastructure bottlenecks faster

Kove is proud to support and help fund the research and standards efforts behind initiatives like NLIP. Advancing open, interoperable AI requires sustained investment — not just in models and software, but in the foundational infrastructure that makes real-world deployment possible. As AI systems move from experimentation to production, the limiting factor is no longer models or orchestration frameworks. It’s the ability to access and scale memory dynamically, without rewriting applications or waiting on new hardware generations. There’s no simpler way to gain that ability today than by virtualizing memory across the data center with software-defined memory.

Read more in the article “Exciting News for the Natural Language Interaction Protocol (NLIP) and its Impact on Implementing AI Agents”: https://bit.ly/45YZry7

DRAM prices are up 200%+. Waiting on new hardware is getting expensive.

CXL is still emerging and requires new servers, new chips, and new architectures.

Kove:SDM™ takes a different path.

- Software-only and available today
- Runs on existing and future x86 infrastructure
- Pools DRAM across the data center with like-local memory performance
- Expands usable memory without buying more high-priced DIMMs

When memory prices spike, architecture matters more than roadmaps.

If you want a real performance and efficiency leap now, software-defined memory delivers it — without forcing you to wait on hardware cycles or pay spot prices for DRAM.

See what Kove:SDM™ makes possible today: https://bit.ly/4bubd7d

Organizations buy more compute, more GPUs, and more hardware…yet workloads still slow down because memory hasn’t kept pace. Kove:SDM™ virtualizes DRAM across servers into a unified, elastic memory pool with latency equivalent to local DRAM, even when memory is served from across the data center. Want to know more about how Kove is delivering the next layer of AI infrastructure? Read our wrap up from AI Infra Summit 2025 to learn about our benchmark results, and see how Software-Defined Memory is enabling memory-bound KV cache workloads to scale significantly larger than has ever been possible before. Get all the info here: https://bit.ly/4psUGE7

Happy 2026! While you’re busy making those resolutions, why not add one about losing those unwanted memory limitations? (Hey, no judgement. Almost every enterprise has them.) This year, let Kove:SDM™ kick your organization's infrastructure performance and tech goals into high gear — with no new hardware or code changes required. This is your year to Achieve More®. Let us show you how: https://bit.ly/4q2496w

Enterprises running real AI workloads know the truth: The true bottleneck isn’t compute, it’s memory. Most latency stalls originate in memory. GPUs frequently idle while waiting for data in memory. And chronically underutilized DRAM remains rigid and tied to individual servers. Too often, organizations buy more compute, more GPUs, and more hardware, yet workloads still slow down — all because memory hasn’t kept pace. But Kove:SDM™ delivers the next layer of AI infrastructure that for too long has been missing. The result is elastic, scalable, like-local memory that eliminates DRAM ceilings. Find out more about the performance that’s possible with Software-Defined Memory, plus get the highlights from Kove CEO John Overton’s AI Infra Summit 2025 keynote here: https://bit.ly/49fLJbi