Optimizing Performance

Performance optimization is a multi-layer effort: choose the right model and precision, optimize inference (batching, quantization, caching), design resilient serving architecture, and measure continuously. This guide summarizes practical techniques and trade-offs to help you increase throughput, reduce latency, and control cost.

• Select the smallest model that meets accuracy requirements — lighter models often provide much better latency and cost.
• Use mixed precision (FP16 / BF16) on GPUs to increase throughput with minimal accuracy loss when supported.
• Quantization (INT8) can dramatically reduce memory and CPU/GPU usage. Validate quality impact for your workload.

// pseudo
model = load_model("model-name", device="cuda", dtype="float16")
// yields ~2x throughput improvement vs float32 on many GPUs

• Choose appropriate compute: CPU for small models or infrequent requests, GPU for large models and high throughput.
• Use model sharding or model parallelism for very large models; use data parallelism (replicas) for throughput scaling.
• Consider multi-tenancy vs single-tenant deployments depending on isolation and tail-latency requirements.

• Batching: group multiple requests into a single inference call to increase GPU utilization. Balance batch size with latency SLOs.
• Dynamic batching: accumulate requests for a short window (e.g., 10–50 ms) then run a batched inference to reduce tail latency.
• Request prioritization and small-batch fast-paths: route latency-sensitive requests to smaller or dedicated workers.
• Caching: cache model outputs for identical inputs (or near-identical via hashing) to avoid repeated inference for common requests.

// Pseudo: accumulate up to N requests or wait T ms, then run batch
batch = []
start = now()
while now() - start < T && batch.size < N:
  batch.append(next_request)
run_inference(batch)

• Define SLOs: p95 latency target, and throughput targets. Design autoscaling rules based on queued requests and GPU utilization.
• Scale horizontally by adding replicas for stateless inference; scale vertically by using larger GPUs for model parallelism.
• Use warm pools or pre-warmed instances to avoid cold-starts when using serverless or on-demand infra.

# scale up when queue length > 50 or GPU util > 80%
if queue_length > 50 or gpu_util > 80:
  replicas += 2
# scale down when queue empty and util < 30% for 5m

• Move CPU-bound preprocessing (image decoding, resizing, tokenization) to dedicated workers or use native optimized libraries.
• Batch CPU preprocessing operations and reuse intermediate artifacts when possible.
• Offload heavy post-processing (e.g., expensive NMS, ranking) to async jobs and return quick placeholders when needed.

• Keep frequently used models resident in memory to avoid load latency; share model instances across threads/processes when safe.
• Use model checkpoints optimized for inference (pruned / quantized artifacts) to reduce memory footprint.
• For multi-model systems, lazy-load models on first use with an eviction policy for low-usage models.

• Instrument latency per stage (queue, preprocess, model, postprocess). Track p50/p95/p99 and tail latencies.
• Collect hardware metrics: GPU/CPU utilization, memory usage, temperature, and IO bottlenecks.
• Use sampling/trace spans to find hotspots; iterate using flamegraphs and profiler output.

• Cache deterministic responses (embeddings, deterministic summaries) and reuse across requests and sessions.
• Use cheaper compute for non-latency-sensitive workloads (batch offline inference) and reserve GPUs for real-time paths.
• Monitor cost per inference and set budgets/alerts; apply model/precision trade-offs when cost exceeds thresholds.

# Example cache key for deterministic text generation
cache_key = sha256(model_name + '|' + prompt + '|' + generation_params)

• Use canary deployments and gradual rollouts when changing models, precision, or batching logic to detect regressions early.
• Run production-like load tests to validate autoscaling and tail-latency behavior under stress.
• Maintain synthetic monitoring (fixed requests) to detect subtle quality regressions after a change.

• Define latency & throughput SLOs (p95, p99 targets).
• Select the smallest model that meets quality needs; try quantization and mixed precision.
• Implement dynamic batching with latency budget; add a small-batch fast-path.
• Cache deterministic outputs and offload heavy post-processing.
• Instrument per-stage metrics and profile regularly; automate alerts for anomalies.
• Use warm pools and autoscaling rules to avoid cold starts and control cost.