8bit.tr Journal
Distributed Inference and Load Balancing: Serving LLMs at Planet Scale
A systems-level guide to distributed inference, load balancing, and traffic shaping for large-scale LLM services.
Why Distributed Inference Is Hard
Global traffic introduces latency, jitter, and hardware heterogeneity.
A single model server architecture collapses under real-world scale.
Load Balancing Strategies
Layer 7 routing can steer requests based on model size, region, or user tier.
Latency-aware routing prevents tail spikes during traffic surges.
Traffic Shaping and Queueing
Queueing policies control bursty traffic and protect SLAs.
Priority queues allow critical requests to bypass lower-value workloads.
Caching and Reuse at the Edge
Edge caches reduce repeated queries and improve latency for common prompts.
Strategic caching can lower GPU demand without sacrificing quality.
Observability and SLOs
Track per-region latency, error rates, and cost per request.
Define SLOs for both average and tail latency to protect user experience.
Failover and Resilience
Plan regional failover paths so traffic can reroute during outages without manual intervention.
Test load shedding policies regularly to ensure they protect critical traffic.
Keep warm standbys in secondary regions to reduce recovery time during incidents.
Use chaos testing to validate that automatic failover behaves as expected.
Track failover success rates so improvements can be measured over time.
Validate recovery time objectives quarterly to ensure they remain achievable.
Monitor queue depths during failover to spot hidden bottlenecks early.
Use synthetic probes in each region to validate routing health continuously.
Review routing tables regularly to ensure failover rules stay accurate.
Define recovery playbooks for partial outages to reduce decision time.
Track customer-facing latency during failover to verify SLA adherence.
Document which services are non-critical so they can be shed first during overload.
Practice regional traffic drain drills to confirm failover scripts work end to end.
Use global capacity forecasts to decide where to pre-scale before seasonal spikes.
Consolidate routing rules into a control plane so updates propagate consistently.
Include regional dependency maps so failover does not cascade to upstream services.
FAQ: Distributed Inference
Do I need multi-region from day one? Not necessarily, but design for it early.
What is the biggest bottleneck? Network latency and GPU availability.
What is the fastest win? Regional routing with simple load shedding.
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