Geo-Distributed LLM Inference in Production: Cross-Region Routing, Wide-Area KV Cache Dynamics, and Data Residency Architecture

Serving large language models at enterprise scale quickly encounters geographic constraints. When users, microservices, and autonomous agents are distributed across North America, Europe, and Asia-Pacific, routing all traffic to a single centralized GPU cluster introduces significant wide-area network (WAN) round-trip times (RTT). An interactive request from Singapore to a US-East data center carries 180ms to 240ms of baseline network latency before the GPU executes a single token.

However, distributing GPU inference clusters across multiple regions introduces a complex distributed systems problem. Unlike stateless microservices, LLM inference is deeply stateful due to the Key-Value (KV) cache. Serving engines rely on prefix caching to skip redundant prefill computations across shared system prompts, multi-turn chat sessions, and retrieved document contexts.

When an incoming request arrives, a multi-region load balancer faces a multi-variable optimization problem: should it route the request to a local GPU cluster that lacks the cached prefix (incurring full prefill compute), or forward it over the WAN to a remote cluster that holds the warm KV cache (incurring network transit latency)?

Recent research and production deployments demonstrate that naive routing policies fail at the tails. Optimizing geo-distributed inference requires jointly modeling wide-area latency, prefix overlap, and continuous batching queue dynamics while respecting regulatory data residency boundaries.

The Latency-Cache Trade-Off: Network RTT vs. Prefill Compute

In single-region deployments, prefix-aware routing algorithms such as RadixAttention in SGLang and chunked prefix matching in vLLM prioritize routing requests to GPU workers that already hold matching KV cache blocks in High Bandwidth Memory (HBM). When a prefix hits the cache, Time-to-First-Token (TTFT) drops dramatically because the engine skips the $O(N)$ transformer forward passes for the cached prompt tokens.

In a geo-distributed setup, this calculation is altered by wide-area network latency.

Consider a request with a prompt length of $L_p$ tokens, where a remote region holds a cached prefix of length $L_{\text{hit}}$ , while the local region has a cold cache ( $L_{\text{hit}} = 0$ ).

The prefill execution time for an uncached prompt of length $L$ can be approximated as:

$T_{\text{prefill}} = \frac{L \times \text{FLOPs/token}}{\text{Effective Prefill TFLOPS}}$

On modern accelerators such as an NVIDIA H100 GPU running a 70B parameter model, chunked prefill throughput typically ranges between 15,000 and 40,000 prompt tokens per second depending on batch sizes and quantization. A 2,000-token prefix takes approximately 50ms to 100ms of prefill execution time.

If forwarding that request from Frankfurt to US-East incurs an intercontinental WAN RTT of 90ms to 120ms, forwarding the request to hit the remote cache yields a net latency loss compared to recomputing the prefill locally:

$\text{Net Latency Delta} = \text{RTT}_{\text{remote}} - (L_{\text{hit}} \times t_p)$

where $t_p$ is the per-token prefill duration. If $\text{RTT}_{\text{remote}} > L_{\text{hit}} \times t_p$ , the network penalty wipes out the computational prefill savings.

Geo-Distributed LLM Routing Architecture

Why Wide-Area KV Cache Synchronization Is Impractical

A common question is whether distributed inference clusters can replicate or synchronize KV cache tensors across geographic regions in the background.

Under modern architectures, the memory footprint of a KV cache is substantial. For a model with $N_{\text{layers}}$ , $N_{\text{heads}}$ , and dimension $d_{\text{head}}$ in 16-bit precision (2 bytes), the KV cache size per token is:

$\text{Bytes per Token} = 2 \times 2 \times N_{\text{layers}} \times N_{\text{heads}} \times d_{\text{head}}$

For a Llama-3-70B model using Grouped-Query Attention (8 KV heads, 64 layers, head dimension 128), storing 1,000 prompt tokens requires approximately 134MB of KV data. For a 32,000-token context, the KV cache exceeds 4.2GB per sequence.

Transmitting gigabytes of raw floating-point tensors across inter-region WAN links with constrained egress bandwidth incurs severe bandwidth costs and transmission delays (100Mbps links take over 300ms just for raw data transfer). Consequently, cross-region KV tensor migration over the WAN is strictly non-viable for interactive inference. Instead, systems must treat KV caches as strictly regional assets and use intelligent routing to steer queries to existing caches.

Comparative Routing Strategies: From Naive Least-Load to Joint Cost Minimization

Multi-region LLM deployments utilize four primary request distribution policies:

1. Geo-DNS / Proximity-Only Routing

Requests are directed strictly to the geographically closest data center via GeoDNS or Anycast BGP routing.

Advantage: Guarantees minimum network transit latency for incoming requests.
Failure Mode: Ignores regional compute saturation and KV cache locality. If a local cluster is experiencing queue buildup or has zero prefix cache hits for a large system prompt, TTFT degrades severely while remote capacity sits idle.

2. Naive Least-Load Routing

Load balancers forward incoming traffic to whichever region has the lowest instantaneous GPU utilization or shortest queue length.

Advantage: Maximizes hardware utilization across global GPU pools and absorbs diurnal traffic swings (e.g., routing European nighttime traffic to US daytime capacity), as explored in SkyWalker.
Failure Mode: Completely ignores prefix cache state. Multi-turn conversations and repeated agent prompts are constantly scattered across different global nodes, resulting in near-zero KV cache hit rates and constant prefill recomputation.

3. Prefix-Similarity (Affinity) Routing

The load balancer inspects the prompt, compares its prefix hash against a distributed index of cached radix tries, and forwards the request to the node with the highest prefix overlap.

Advantage: Maximizes KV cache hit rates across the cluster.
Failure Mode: Pathological cross-region forwarding. A load balancer will route a request across oceans to save a 500-token prefill, adding 200ms of WAN transit delay to save 15ms of GPU execution time. Furthermore, hot prefixes can cause severe single-region traffic hotspots.

4. Joint Cost Optimization (The GORGO Model)

Recent research introduced by GORGO (Toniolo et al., 2026) demonstrates that optimal geo-distributed routing requires an additive joint cost function evaluated per candidate region $r$ :

$\text{Cost}(r) = \text{NetworkLatency}(r) + t_p \times (L_p - L_{\text{hit}, r}) + \hat{q}_s \times \text{QueueWaitTime}(r)$

In this model:

$\text{NetworkLatency}(r)$ is the continuously measured RTT to the load balancer in region $r$ .
$t_p \times (L_p - L_{\text{hit}, r})$ represents the residual prefill execution cost for uncached tokens.
$\hat{q}_s \times \text{QueueWaitTime}(r)$ accounts for admission queuing and continuous batching saturation in region $r$ .

Empirical benchmarks show that evaluating this joint cost function reduces P99 TTFT significantly compared to prefix-only or least-load routing by suppressing unnecessary cross-region hops while still taking advantage of remote caches when prompt sizes are large ( $L_p > 8,000$ tokens).

Ingress Topologies: Edge Gateways vs. Centralized Proxies

Deploying geo-distributed LLM routing involves choosing between two architectural control planes:

Topology A: Distributed Edge Gateways with State Peering

Each region runs an independent ingress gateway (e.g., Envoy or a custom Go proxy) close to local users. Gateways maintain local prefix radix trees and exchange summarized telemetry (measured peer RTT, queue depths, and top prefix hashes) via lightweight gossip or low-frequency gRPC streams.

Pros: Zero added latency for strictly local requests; no single point of failure.
Cons: Gossip synchronization delays can lead to slightly stale peer state representations.

Topology B: Centralized Global Router / Inference Profiles

Cloud providers such as Amazon Bedrock Cross-Region Inference use centralized inference profiles. Requests hit an edge ingress and are dispatched across predefined region pools (e.g., us-east-1, us-west-2, eu-central-1). Bedrock dynamically manages throughput quotas and routes requests across regions to bypass local capacity throttles.

Pros: Global visibility over token quotas, system-wide load smoothing, and automatic failover.
Cons: Added control-plane hop if the router is not co-located with the ingress point.

Data Residency, Sovereignty, and Compliance Boundaries

In enterprise and government deployments, pure latency/cost optimization must operate within strict legal boundaries. Under regulations such as the EU General Data Protection Regulation (GDPR), HIPAA in the United States, and financial data sovereignty laws in Switzerland and Singapore, user prompt text and generated completions cannot leave designated geographical borders.

Production architectures enforce residency constraints through hierarchical policy filtering:

Hard Residency Filtering: Before evaluating network latency or prefix cache metrics, the load balancer applies hard compliance tags. If a request carries an EU-ONLY residency tag, candidate serving nodes outside the EU (e.g., US-East, Tokyo) are removed from the routing candidate set.
Regional Isolation Pools: Cloud gateways structure routing into Geographic Profiles (e.g., AWS Bedrock's European cross-region profile containing Dublin, Frankfurt, and Paris) versus Global Profiles.
Zero-Data-Retention (ZDR) Enclaves: When cross-region spillover is permitted for compute processing, prompt payloads must be processed ephemerally in memory without persistent disk logging in transit regions.

Production Implementation Checklist

For platform teams architecting a multi-region LLM serving infrastructure:

Deploy Model Runtimes with Prefix-Aware Metrics: Ensure serving engines (vLLM, SGLang, TensorRT-LLM) expose metrics for cached block counts, prefill token latency ( $t_p$ ), and pending queue slots over Prometheus or gRPC.
Benchmark Regional RTT Matrix: Maintain a real-time matrix of inter-region network round-trip times. Network latency between cloud regions varies dynamically based on transit congestion.
Calculate the Cross-Region Break-Even Threshold: Establish the minimum token threshold required before considering a cross-region forwarding decision:

$L_{\text{threshold}} = \frac{\text{RTT}_{\text{peer}}}{t_p}$ For a peer with 100ms RTT and an engine prefill rate of 50 tokens/ms ( $t_p = 0.02\text{ms}$ ), never forward a request unless the expected prefix cache hit exceeds 5,000 tokens.

Implement Circuit Breakers for WAN Outages: If inter-region latency spikes or error rates exceed 1%, immediately fall back to local execution and disable cross-region routing.
Enforce Tenant-Level Data Sovereignty Flags: Tag all incoming API requests with compliance scopes to ensure automated load balancing never violates data residency requirements.