Batch Inference

Benched.ai Editorial Team

Batch inference processes multiple inputs in a single model forward pass, amortizing overhead and improving throughput.

Throughput vs Latency

Batch Size	GPU Utilization	Latency Impact
1	25 %	Minimal
8	70 %	+20 % tail latency
32	90 %	+80 % tail latency

Scheduling Strategies

Strategy	How It Works	Best For
Fixed window	Collect requests for N ms	Predictable traffic
Token bucket	Add tokens per time unit; fill bucket with requests	Bursty workloads
Dynamic	Adjust batch size based on queue depth	Mixed latency tiers

Design Trade-offs

Larger batches yield lower cost per token but lengthen service time.
Mixing user tenants risks one slow request delaying others (the head-of-line problem).
Batching incompatible prompt lengths may waste tokens due to padding.

Current Trends (2025)

Model-serving frameworks auto-tune batch size per GPU based on recent latency targets.
Sequence-parallel decoding lets servers interleave generation for different requests without large padding.
Multi-model batching (M2B) co-hosts small models in the same kernel launch.

Implementation Tips

Start with 50 ms batching window; empirically tune per model.
Pad sequences to the nearest multiple of 8 tokens to align tensor core tiles.
Drop to batch size 1 when p99 latency SLA violated for >30 s.
Emit per-batch metrics: size, average token count, compute time.