Pliops FusIOnX: Bypassing HBM Limits for GPU Inference

Table of Contents

🚀 The HBM Bottleneck in Modern GPU Servers
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High-bandwidth memory (HBM) has become the primary performance limiter for large-scale AI inference. As large language models push toward longer context windows and higher concurrency, even the most advanced GPUs quickly run out of on-package memory.

Once a model’s context window exceeds available HBM, inference performance collapses. Key-value (KV) cache entries are evicted, recomputed, and reloaded repeatedly, causing latency spikes and sharply reduced throughput.

Pliops is targeting this exact pain point.

🧠 FusIOnX and the XDP LightningAI Card
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Pliops has unveiled FusIOnX, an end-to-end inference acceleration stack built around its XDP LightningAI PCIe card. Rather than replacing GPUs or requiring proprietary memory fabrics, LightningAI introduces a sub-HBM memory tier using ultra-fast NVMe and RDMA-accessed SSDs.

The card is powered by a purpose-built ASIC and software stack that transparently augments GPU memory for inference workloads.

At its core, LightningAI functions as:

A key-value cache extension for LLM inference
A shared memory tier across one or more GPU servers
A vendor-agnostic accelerator, independent of GPU supplier or storage backend

⚡ Accelerating vLLM and Nvidia Dynamo
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Modern inference frameworks rely heavily on KV caching. UC Berkeley’s vLLM, widely used for high-throughput serving, stores intermediate attention states to avoid recomputation. Nvidia’s Dynamo framework orchestrates inference engines such as TensorRT-LLM and vLLM for optimal scheduling.

Pliops integrates directly into this software layer.

Key results claimed by Pliops:
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2.5× higher requests per second for standard vLLM production stacks
Up to 8× faster end-to-end inference in memory-constrained scenarios
Reduced latency growth as context windows scale

By storing already-computed KV cache entries on fast SSDs instead of recomputing them after eviction, FusIOnX allows inference performance to scale beyond native HBM capacity—without adding more GPUs.

🧩 FusIOnX Stack Variants
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Pliops positions FusIOnX as “AI stack glue,” offering multiple deployment models:

FusIOnX vLLM Production Stack
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vLLM KV-cache acceleration
Smart request routing across multiple GPU nodes
Full upstream vLLM compatibility

FusIOnX vLLM + Dynamo + SGLang
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Integrated KV-cache acceleration
Support for prefill/decode node separation
Single-node and multi-node configurations

FusIOnX KVIO
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Distributed Key-Value I/O over the network
Serves any GPU within a server
Planned support for RAG and vector databases on CPU servers

FusIOnX KV Store
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XDP AccelKV distributed key-value store
RAIDplus self-healing storage
Designed for long-term LLM memory persistence

This architecture is particularly well-suited for emerging models with persistent memory concepts, such as Google’s Titans.

🧱 Deployment Models: Disaggregated or Hyperconverged
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The XDP LightningAI card can be deployed in two primary ways:

Disaggregated Mode
Accelerates GPU servers connected to external storage arrays or shared data pools.
Hyperconverged “LLM-in-a-Box” Mode
Installed directly inside a GPU server with 24 SSD slots, providing both storage and inference acceleration in a single system.

Pliops demonstrates this configuration in a 2RU Dell server, positioning it as a turnkey inference appliance.

🔮 What’s Coming Next
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Pliops is expanding FusIOnX beyond LLM inference:

FusIOnX RAG & Vector Databases
Proof-of-concept stage, targeting accelerated index build and retrieval.
FusIOnX GNN
Designed to store and retrieve node embeddings for large-scale graph neural networks.
FusIOnX DLRM
Focused on deep learning recommendation models, enabling TB–PB scale embedding access with simplified storage pipelines.

🆚 Competitive Landscape
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Pliops is not alone in tackling memory pressure for AI workloads. Competing approaches include:

GridGain — Distributed in-memory data grids for AI and RAG pipelines
Hammerspace Tier Zero — Unified data access across memory tiers
WEKA Augmented Memory Grid — Memory pooling with GPUDirect support
VAST Data VUA — Unified storage and memory abstraction for GPU workloads

What differentiates Pliops is its PCIe add-in card approach, offering memory-tier expansion without forcing architectural lock-in or cluster-wide reconfiguration.

💡 Final Takeaway
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As LLM context windows grow faster than GPU HBM capacity, inference is becoming a memory problem, not a compute problem.

Pliops’ FusIOnX and XDP LightningAI card address this gap by inserting a fast, scalable memory tier beneath GPUs—reducing recomputation, stabilizing latency, and extending the useful life of existing GPU infrastructure.

For operators running large-scale inference, this approach may prove cheaper—and nearly as fast—as simply buying more GPUs.