To optimize effectively, you must understand the two distinct phases of LLM inference:

1. Describe the goal: You start with an unoptimized model deployment
2. Apply quantization: Reduce model weights (such as, to 4-bit) to fit larger batches in memory
3. Optimize attention: Use FlashAttention or Grouped-Query Attention (GQA) to minimize memory movement costs
4. Manage memory: Implement PagedAttention to store KV caches in non-contiguous blocks, eliminating fragmentation
5. Execute and monitor: Deploy with in-flight batching to immediately start new requests as others finish

Here’s how optimized inference compares to traditional "naive" model serving:

Google Cloud offers a range of tools designed for different skill levels and architectural needs.

Model Garden is the fastest path to deploying optimized versions of leading open models like Llama, Gemma, and Mistral.

Go to Model Garden and find a supported OSS model. Click Deploy. In the configuration, select an Optimized Runtime such as vLLM or NVIDIA NIM.

Choose a quantized version of the model (for example, 4-bit or 8-bit) to reduce its memory footprint. This allows you to serve larger batch sizes on the same GPU, directly increasing throughput.

Ensure the serving container is configured to use PagedAttention. This technique allows the model to store its "memory" (Key-Value cache) in non-contiguous blocks, preventing memory wastage and allowing for longer context windows.

Once deployed, Vertex AI automatically handles in-flight batching, processing new requests as soon as an existing request completes a token. Use Vertex AI Model Monitoring to track latency and ensure the "vibe" of the output remains high quality.

For teams needing granular control over their orchestration and custom inference kernels, GKE is the industry-standard choice.

Provision a GKE cluster with specialized GPU nodes (such as, L4 or H100). Install the NVIDIA GPU Operator to handle driver management and performance tuning automatically.

Use a containerized inference engine like vLLM or Triton Inference Server. These servers support continuous batching and tensor parallelism, allowing you to shard large models across multiple GPUs. vLLM also gives you the ability to switch between TPUs and GPUs, with minimal additional coding.

For mission-critical latency needs, configure speculative decoding. This involves using a smaller, faster "draft" model to predict tokens, which are then verified in parallel by your larger "target" model, often providing a 2x-3x speedup.

GKE Inference Quickstart acts as a pre-configured database of tested inference stack configurations. By specifying your model, latency requirements, and cost priorities, the tool provides a set of recommendations based on best practices and the latest benchmarks. This allows you to monitor inference-specific performance metrics and dynamically fine-tune your deployment to ensure it always runs on optimized technology.

GKE Inference Gateway is now generally available, introducing two advanced capabilities for managing complex GenAI applications.

• Prefix-aware routing: For applications like multi-turn chat or document analysis, this feature routes requests to the same accelerators that already have the context cached, improving response times.
• Disaggregated serving: This technique separates the initial "prefill" phase (prompt processing) from the "decode" phase (token generation). Because these stages have different resource needs, you can run them on separate, optimized machine pools to maximize efficiency.

Anywhere cache is a new, fully consistent read cache that works with existing Google Cloud Storage (GCS) buckets to cache data within the same zone as your accelerators. This reduces read latency by up to 96% and minimizes the network costs associated with read-heavy workloads.

Tying the entire infrastructure together is Cloud WAN, a fully managed global network built on Google's planet-scale infrastructure. Cloud WAN connects AI computing resources across different regions, clouds, and on-premises environments, delivering a 40% improvement in inference application experience and 40% lower TCO compared to traditional WAN solutions.