How to Optimize LLM Inference for Throughput and Cost (Real Production Strategies)

Getting an LLM running in production is only the first step.

The real challenge is making it efficient.

In practice, most teams struggle with:

• low GPU utilization
• high latency under load
• rising infrastructure costs

Optimizing inference is about balancing all three.

What Optimization Actually Means

Optimization is not just about making models faster.

It’s about improving:

• throughput (requests per second)
• latency (time per request)
• cost per token

These are tightly connected.

Improving one often impacts the others.

For a deeper look at how these systems are structured, see how inference systems actually run in production.

1. Batching (The Biggest Lever)

Batching is the single most important optimization for inference systems.

Instead of processing requests individually, systems group multiple requests into a single batch.

This leads to:

• higher GPU utilization
• more tokens processed per second
• lower cost per request

But batching introduces tradeoffs:

• Larger batches → higher throughput
• Smaller batches → lower latency

The goal is finding the right balance for your workload.

For a deeper breakdown, see how batching strategies work in production systems.

2. GPU Selection and Utilization

Not all GPUs perform the same for inference.

Key factors:

• memory (VRAM)
• compute capability
• cost per hour

For example:

• Larger models require more VRAM
• Smaller models may run efficiently on cheaper GPUs

But the biggest issue is not GPU choice — it’s utilization.

Many systems:

• run GPUs at 20–40% utilization

This happens due to:

• poor batching
• idle time between requests
• inefficient scheduling

Optimizing utilization often has a bigger impact than upgrading hardware.

3. Efficient Inference Engines

The inference engine determines how well your system uses hardware.

Popular options include:

• vLLM
• TensorRT-LLM
• SGLang

These engines optimize:

• memory usage (KV cache reuse)
• token generation speed
• parallel execution

Even with the same model, performance can vary significantly depending on the engine.

4. KV Cache Optimization

Modern inference systems rely heavily on KV cache.

KV cache:

• stores intermediate computations from previous tokens

This allows:

• faster generation
• reduced recomputation

But it introduces challenges:

• memory fragmentation
• cache eviction strategies
• scaling across requests

Efficient KV cache management is critical for performance at scale.

5. Request Scheduling and Load Balancing

Optimization doesn’t stop at the GPU.

Upstream systems play a major role.

Schedulers are responsible for:

• grouping compatible requests
• assigning workloads to GPUs
• minimizing idle time

Load balancing ensures:

• requests are distributed evenly
• no single GPU becomes a bottleneck

In agent-based systems like OpenClaw or NemoClaw, this becomes even more important, as workloads are dynamic and unpredictable.

6. Scaling Strategy (Horizontal vs Vertical)

Scaling is a key part of optimization.

Horizontal Scaling

• add more GPUs
• distribute workloads across nodes

Vertical Scaling

• use more powerful GPUs
• increase batch size per node

Most production systems use a combination of both.

The challenge is:

• scaling efficiently without increasing cost disproportionately

7. Cost Optimization Strategies

At scale, cost becomes a major constraint.

Common strategies include:

• using spot instances where possible
• mixing GPU types for different workloads
• scaling down during low demand
• optimizing batch size

The goal is not just performance — it’s:

• performance per dollar

8. Why Optimization Matters for AI Agents

AI agent systems (like OpenClaw and NemoClaw) amplify these challenges.

Because:

• agents generate multiple inference calls
• workloads spike unpredictably
• tasks can chain together

This leads to:

• significantly higher compute demand

Without optimization, costs can scale rapidly.

Final Thoughts

Inference optimization is where real-world AI systems are won or lost.

It’s not about:

• running a model

It’s about:

• running it efficiently at scale

Teams that understand this can:

• serve more users
• reduce costs
• improve performance

Those that don’t quickly run into bottlenecks.