Kove

Experience the power of our ecosystem at !

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

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