Distributed Inference with NVIDIA Triton

Distributed InferenceNVIDIA TritonSLA Engineering

A Triton-inspired system design and simulation that conceptually explores how modern inference platforms scale horizontally under high concurrency while reasoning about strict p95 / p99 latency SLAs. The project isolates the core behaviors that determine inference stability at scale — request queueing, dynamic batching, model instance parallelism, and cluster-level routing — without relying on GPU hardware.

Triton provides powerful queuing and dynamic batching mechanisms — but p99 latency only improves when batching and routing are planned together. Otherwise, batching can simply increase queue delay and violate SLAs.

Key Skills Demonstrated

Distributed inference system design
Concurrency and tail-latency analysis (p95 / p99)
Dynamic batching tradeoff reasoning
Load balancing and routing strategies
LLM-aware inference architecture intuition

Core Concept Snippet

scheduler.py

# conceptual Triton-style request scheduling (system-level intuition)

for request in incoming_requests:
    node = select_node(nodes, policy="least_queue")
    node.queue.append(request)

for node in nodes:
    batch = form_dynamic_batch(
        queue=node.queue,
        max_batch_size=8,
        batch_window_us=2000
    )
    instance = node.next_free_instance()
    instance.run(batch)

What This Snippet Shows

This snippet illustrates the core mechanics behind distributed inference platforms like Triton. Requests are routed across nodes, queued locally, dynamically batched to amortize execution overhead, and dispatched to the next available model instance. In practice, tail latency is dominated by queueing and batching decisions rather than raw compute.

Key Takeaways

p99 latency is a distributed systems problem, driven by queue imbalance and routing decisions — not just GPU speed.
Dynamic batching improves throughput but introduces queue delay that can dominate tail latency.
Routing improves median latency first; p99 only improves once decode variance or saturation emerges.
Triton enables flexible throughput/latency tradeoffs, but meeting SLAs requires intentional tuning.

Conclusion