Eight Use Cases for RobustMQ in AI Scenarios

In the previous post we discussed RobustMQ's four core technical designs: object storage data source with intelligent cache, million-scale lightweight Topics, shared subscription, and multi-mode storage. These aren't abstract concepts—they map to concrete use cases. This article expands on those scenarios and explains how RobustMQ is used in the AI era.

Scenario 1: Training Data Cache Acceleration

This is RobustMQ's most central value in AI scenarios.

Training data for large models is typically stored in S3 or MinIO, from hundreds of GB to tens of TB. During training, 128 GPUs consume data at hundreds of MB per second per GPU. The traditional approach: each GPU process reads directly from S3; 128 concurrent requests hit S3 with 50–200 ms latency; GPUs spend much time waiting. At $2–3/hour per A100, 30–40% of time wasted on data wait means thousands of dollars per day on a 64-card cluster.

RobustMQ's approach: create a Topic pointing to the S3 data path. RobustMQ scans files, builds indexes, and uses a three-tier cache (memory/SSD/S3) for intelligent prefetch. Training processes consume via standard Kafka Consumer API; latency drops from 200 ms to 2 ms.

Key benefit: multi-epoch training. In the first epoch, RobustMQ loads data from S3 into cache; some requests still hit S3. RobustMQ learns access patterns—training data is sequential and predictable. By the second epoch, it knows what's needed next and prefetches. By the third epoch, 95%+ of accesses come from cache.

Multi-node scenarios benefit even more. 16 servers, 128 GPUs, 10 epochs—if each reads S3 directly, the same data is downloaded 160 times. RobustMQ as a shared cache layer downloads from S3 once and distributes over the internal network. S3 egress drops by two orders of magnitude and avoids S3 throttling. New nodes join with cache already ready; zero warm-up.

Core design: Object storage data source and intelligent cache.

Scenario 2: AI Agent Independent Communication Channels

AI applications in 2025 are moving from monolithic models to multi-Agent collaboration. A typical Agent system may have hundreds or thousands of Agents: retrieval, planning, code generation, verification. These Agents need frequent communication—exchanging messages, syncing state, passing tasks.

The traditional approach: all Agents share a few Kafka Topics and use message metadata to distinguish Agents. At scale: messages mix, monitoring is hard; permissions are coarse; cost attribution is vague; one slow Agent can drag down the whole Topic.

Ideal: each Agent has its own channel. But Kafka's Topic limit (file descriptors, disk I/O) makes tens of thousands of Topics the practical ceiling. 100,000 Agents × 3 Topics (input/output/state) = 300,000 Topics—a Kafka cluster would collapse.

RobustMQ's unified RocksDB storage keeps all Topics in one KV instance. Creating a Topic is adding a metadata record; no new files—seconds. 300,000 Topics is manageable. Each Agent has its own message space; monitoring is per-Agent; permissions are per-Topic; cost can be attributed by Topic.

For multi-tenant Agent platforms this matters. A SaaS platform serving thousands of customers, each with dozens to hundreds of Agents, could reach hundreds of thousands of Topics. RobustMQ supports this; Kafka does not.

Core design: Million-scale lightweight Topics.

Scenario 3: Elastic GPU Training

Cloud training is characterized by dynamic resources. Tasks may use different GPU counts at different times; Spot instances reduce cost but can be reclaimed; resources may scale based on training progress.

Kafka's concurrency equals Partition count. With 8 Partitions, at most 8 consumers. Scaling from 8 to 32 GPUs? Either recreate the Topic with more Partitions (affecting running training) or have multiple GPUs share one Partition's data (requiring custom distribution logic).

RobustMQ's shared subscription decouples storage sharding and compute concurrency. One Partition can be consumed by any number of consumers; concurrency is driven by consumer count, not Partition count.

Spot training: GPU Spot instances consume from RobustMQ; when reclaimed, RobustMQ records consumed position; when instances return, training resumes from that point without reprocessing.

Dynamic scaling: start with 8 GPUs; loss drops slowly, scale to 32. Add 24 consumers to the same shared subscription group; RobustMQ load-balances. No Topic changes, no restart. Near the end, release 24 GPUs; 8 continue; RobustMQ rebalances.

The training client always uses the standard Kafka Consumer API; concurrency is no longer bound by Partition count.

Core design: Shared subscription.

Scenario 4: Multi-Stage Training Data Pipeline

AI training involves complex data processing: raw read, cleaning, augmentation (random crop, color adjust, flip), feature extraction, tokenization, then GPU training. Traditionally these are coupled in the PyTorch DataLoader—hard to debug, limited extensibility, CPU preprocessing blocks GPU training.

Use RobustMQ to decouple each stage:

S3 raw data → Topic-raw → cleaning Worker → Topic-clean → augmentation Worker → Topic-augmented → GPU training

Each stage scales independently. Augmentation is CPU-heavy—add 10 augmentation Workers. GPU training needs more data—add GPU nodes. They don't interfere.

Each stage debugs independently. Data quality issue? Inspect Topic-clean. Augmentation strategy wrong? Change only the augmentation Worker. Want to try a new Tokenizer? Add a new processing node and A/B test.

Each stage can replay independently. Found a bug in cleaning? Reconsume from Topic-raw, rerun cleaning; downstream augmentation and training pick up corrected data. No full pipeline restart.

This architecture helps fast-iterating AI teams. Experiment cycles shrink from "change code → restart training → wait hours for result" to "change one stage → verify in minutes → continue."

Each pipeline stage is an independent consumer/producer; shared subscription lets each stage scale independently; million Topics give independent storage for intermediate state.

Core design: Shared subscription + million-scale Topics.

Scenario 5: One Cluster for Multiple AI Loads

Different AI scenarios need different data characteristics; one storage strategy can't fit all.

Gradient sync: memory latency (microseconds), can lose data—GPUs compute gradients, send to RobustMQ, other GPUs consume and aggregate for parameter updates. A lost gradient is compensated in the next batch. Use Memory storage; pure memory; lowest latency.

Training data: repeated access, temporary—one pass per epoch, 10 epochs = 10 passes; data can be cleared after training. Use Hybrid (memory + SSD); hot in memory, warm on SSD; balance latency and capacity.

Model checkpoints: permanent storage—periodic model snapshots for recovery or rollback. Checkpoints can be hundreds of GB. Use Persistent; write to disk; multiple replicas for durability.

Training logs and metrics: low-cost archival—loss curves, learning rate, GPU utilization; small volume but long retention for analysis. Use Tiered; recent on SSD; auto-tier to S3 for long-term archival.

RobustMQ's per-Topic storage config lets one cluster serve all four. Create Topics with the right mode; the system adapts. No need for separate Redis for gradient sync, Kafka for training data, S3 Gateway for log archival. One RobustMQ, full coverage.

Core design: Multi-mode storage and tiering.

Five Scenarios, Four Designs

Looking back at these five scenarios, each maps directly to one or more core designs; each is something RobustMQ can do well that Kafka cannot:

Object storage source and intelligent cache solve training data acceleration—Kafka Brokers must hold local copies and can't use S3 natively as source. Million-scale Topics solve Agent communication isolation—Kafka's ceiling is tens of thousands. Shared subscription solves elastic training and pipeline scaling—Kafka's concurrency is locked to Partition count. Multi-mode storage solves heterogeneous load—Kafka has only append-only log.

All scenarios share one premise: compatible with and enhanced beyond the Kafka protocol. Training frameworks, Agent apps, data processing tools—all Kafka-based clients connect with zero changes. Users see familiar APIs and get beyond-Kafka capabilities.

That's RobustMQ's role in the AI era: not replacing a single tool, but providing unified communication infrastructure that covers training data cache, Agent communication, elastic scheduling, and heterogeneous load—all in one.