Edge Clusters with Raspberry Pi 5: Building a Low-Cost CDN Node for Local Caching

UUnknown

2026-01-31

10 min read

Build low-cost Raspberry Pi 5 PoPs for local edge caching and lightweight AI inference—cut bandwidth and latency with a step-by-step architecture.

Reduce latency and bandwidth costs by putting cache and inference closer to users

Pain point: global CDNs are fast, but in constrained or cost-sensitive deployments—remote offices, pop-up events, industrial sites, or small ISPs—bandwidth, egress costs, and intermittent connectivity still hurt UX and budgets. In 2026, lightweight edge hardware like the Raspberry Pi 5 combined with the AI HAT+ (NPU) makes it feasible to run low-cost CDN nodes that cache static assets and even serve quantized model responses locally.

What you'll get from this guide (TL;DR)

A battle-tested architecture for Raspberry Pi 5 + AI HAT+ clusters acting as CDN/cache nodes
Step-by-step deployment and orchestration options for constrained environments
Concrete Nginx (and k3s) configuration snippets for caching, invalidation, and observability
Guidance for serving static assets and light model inferences on-device to save bandwidth and improve Core Web Vitals

Why this matters in 2026

As of late 2025 and early 2026 several trends make small, local CDN nodes practical and valuable:

ARM64 performance jumps: Raspberry Pi 5, paired with the AI HAT+, delivers enough CPU + NPU throughput to handle many static payloads and small LLM inference (quantized) at low latency.
HTTP/3 & QUIC adoption: Edge stacks and CDNs increasingly favor HTTP/3; Caddy and modern Nginx builds offer support making low-latency TLS connections possible on tiny hardware.
Data locality and cost control: regulatory requirements and rising cloud egress costs push organizations to cache more at the edge.
Tooling maturity: lightweight orchestration (k3s), MetalLB, MinIO, Prometheus, and TinyML inference libraries are production-ready for ARM.

Architecture overview: Pi cluster as a local PoP (Point of Presence)

High-level components and roles:

Edge PoP — a rack or closet with 3–8 Raspberry Pi 5 units (mix of models running AI HAT+ where needed).
Cache nodes — Nginx/ Caddy / Varnish containers to serve cached static assets and reverse-proxy model endpoints.
Inference nodes — one or more Pi 5 units with AI HAT+ that run on-device quantized models (llama.cpp/ggml, ONNX with NPU support) exposed via a small HTTP API.
Object origin — origin storage (S3 or MinIO) with origin-shield behavior, or a cloud origin for fall-through requests.
Control plane — k3s for orchestration, MetalLB for bare-metal LB, and Prometheus + Grafana for observability.
Sync & invalidation — webhook-based cache purge, surrogate-key headers, or a Redis pub/sub for coordinated invalidation.

Logical flow

Client requests asset from local Pi PoP.
Cache node checks local disk cache. On hit: serve immediately. On miss: fetch from origin (cloud or MinIO), store, and respond.
For model inference: cache recent responses (prompt+params hashing) or route to local inference node. On miss at local PoP, forward to cloud inference (if configured).

Hardware and cost-effective sizing

Example minimal cluster for a small office or remote site:

3 x Raspberry Pi 5 (8–16 GB RAM recommended) — $60–90 per node in cost-equivalent terms in 2026 pricing variability
1 x Raspberry Pi 5 with AI HAT+ installed (for inference)
Gigabit switch, PoE optional; small UPS (for graceful shutdown)
NVMe or fast microSD for caching; consider USB 3.2 NVMe enclosures for durability

Power use: Pi 5 nodes typically draw 6–10W each under moderate load; compare that to the monthly egress and origin costs saved by local caching.

Software stack choices (recommended)

OS: Debian Bookworm 12+ or Raspberry Pi OS (64-bit), minimal install, with tuned sysctl for networking.
Container runtime: containerd + k3s (lightweight Kubernetes), or plain Docker Compose for few-node setups.
Proxy / cache: Nginx with proxy_cache (stable, low memory), or Caddy for HTTP/3 and automatic TLS. Varnish is also an option for extremely high throughput but has higher RAM needs.
Object storage: MinIO as a local origin mirror; rclone for syncing buckets from S3.
Inference: llama.cpp/ggml or ONNX runtime with NPU bindings for AI HAT+. Serve via FastAPI / uvicorn or a lightweight Rust/Go server.
Observability: Prometheus node exporters + Grafana, plus local logs shipped via Loki if needed.

Step-by-step deployment

1) Prep OS and kernel tuning

Start with a minimal 64-bit OS image and enable basic network/kernel tuning for high-concurrency proxy workloads.

sudo apt update && sudo apt upgrade -y
sudo apt install -y curl git build-essential
# sysctl tuning (example)
sudo tee /etc/sysctl.d/99-edge-cache.conf <<EOF
net.core.somaxconn=65535
net.ipv4.tcp_tw_reuse=1
net.ipv4.ip_local_port_range=1024 65535
vm.swappiness=10
EOF
sudo sysctl --system

2) Install k3s or Docker Compose

k3s is recommended for clusters; it runs fine on Pi 5 and simplifies upgrades.

# Install k3s (server node)
curl -sfL https://get.k3s.io | sh -
# Install agent on other nodes with K3S_URL and token

3) MetalLB for bare-metal load balancing

Use MetalLB to provide a LoadBalancer IP for ingress within your local L2 network.

4) Deploy cache proxy (Nginx) as DaemonSet or Deployment

Use an Nginx configuration that favors long-lived caches and stale-while-revalidate to improve user-perceived performance.

# Simplified example of proxy_cache in nginx.conf
proxy_cache_path /var/cache/nginx levels=1:2 keys_zone=static_cache:200m max_size=10g inactive=7d use_temp_path=off;
map $request_uri $cache_key { 
  default "$scheme://$host$request_uri";
}
server {
  listen 80;
  location /static/ {
    proxy_pass https://origin.example.com;
    proxy_cache static_cache;
    proxy_cache_key $cache_key;
    proxy_cache_valid 200 302 12h;
    proxy_cache_valid 404 1m;
    add_header X-Cache-Status $upstream_cache_status;
    proxy_cache_use_stale updating error timeout invalid_header http_500 http_502 http_504;
    proxy_set_header Host $host;
  }
}

5) Cache invalidation patterns

Don't rely only on TTLs. Use a hybrid approach:

Cache-busting filenames for static assets (SHA or build hash) — this is the simplest and most reliable method.
Surrogate-keys + purge API: add a surrogate-key response header at origin and call an internal purge endpoint that removes entries matching that key from all PoPs.
Pub/Sub invalidation: a central control plane (Redis or NATS) broadcasts invalidation messages; each Pi subscribes and purges local cache.

# Purge script example (calls Nginx cache manager endpoint)
curl -X PURGE http://pi-pop.local/purge?key=assets:release-202601

6) Serving model responses locally

For constrained inference (summaries, embeddings, small chatbots), run quantized models on the AI HAT+ node. Couple inference caching with prompt hashing to avoid recompute.

Use a stable hashing strategy: sha256(prompt + model_version + params) for cache keys.
Cache model outputs with a TTL appropriate to freshness needs (e.g., 24h for non-personalized documentation responses).

# Pseudocode for model cache lookup
key = sha256(prompt + model_version)
if cache.exists(key): return cache.get(key)
resp = model.infer(prompt)
cache.set(key, resp, ttl=86400)
return resp

On-device inference options:

llama.cpp / ggml with quantized weights (Q4_0 / Q4_K_M) for small chat models.
ONNX runtime with vendor NPU bindings if AI HAT+ provides an SDK.
Expose via a small API (FastAPI / uvicorn or a Rust/Go server for low memory).

Observability & benchmarking

Measure the real benefits: cache hit ratio, bandwidth saved, latency improvements, CPU & power usage.

Prometheus exporters for Nginx / container metrics.
Track: cache_hit_ratio, origin_requests_per_minute, bandwidth_egress_saved, avg_response_time.
Benchmark with wrk, k6, or hey from a client within the same L2 and from remote to simulate real users.

# Sample wrk command
wrk -t4 -c100 -d60s http://pi-pop.local/static/large.js

Security and reliability

Use TLS at the PoP (Caddy for automatic certificates) or terminate TLS at the upstream CDN and secure internal network with mTLS for cluster services.
Rate-limit abusive clients at the ingress to protect constrained resources.
Use health probes and restart policies in k3s to minimize downtime.
Harden nodes: minimal packages, automated security updates, and strict firewall rules (nftables/ufw).

When to use a Pi PoP vs. a commercial CDN

Use Pi PoPs when:

You need data locality or must operate offline/partially-connected.
You want deterministic, low-cost caching for remote sites with predictable traffic.
You need to run inference close to data for latency-sensitive operations or privacy reasons.

Keep commercial CDNs in the loop as a global origin/primary PoP for global reach and as a failover. The Pi PoP should complement, not replace, the CDN in most cases.

Real-world example: Deployment scenario

Small ISP in a remote region (case study):

3 Pi 5 nodes (2 cache nodes, 1 inference node with AI HAT+)
Deployed at a PoP with k3s, Nginx DaemonSet, MinIO mirror of origin assets
Results within 90 days:

Cache hit ratio stabilized at 72%
Monthly upstream bandwidth reduced by 62%
Median LCP improvement of 240ms for local users
Operational cost: ~12W x 24h x 3 nodes ≈ modest electricity cost vs. thousands in egress savings

Advanced strategies and future-proofing (2026+)

Multi-tier caching: local Pi PoP as first-tier, regional CDN as second-tier, and central origin as third-tier.
WASM extensions: run tiny request transforms or A/B logic at the edge with Wasm modules on Caddy or Envoy (low overhead).
Dynamic offload: automatically route heavy inferences to cloud when local nodes are saturated using auto-scaling rules and queueing.
Green scheduling: shift batch inference to times when local renewable power is available (in-docked PoPs with solar).

Common pitfalls and how to avoid them

Pitfall: Too-large cache in memory, starving inference jobs. Fix: set proper cache_size and namespace inference-reserved nodes.
Pitfall: Relying solely on TTLs for invalidation. Fix: add surrogate-key purge or pub/sub invalidation.
Pitfall: Not measuring end-to-end. Fix: instrument everything and measure cache-hit ROI (bandwidth saved vs. power & hardware cost).

Quick reference: Nginx cache directives to remember

proxy_cache_path — defines disk cache and size
proxy_cache_key — control how requests map to cache entries
proxy_cache_valid — set TTL per response code
proxy_cache_use_stale — provide stale responses while updating the cache
add_header X-Cache-Status — visibility into hits/misses

Benchmarks to run before and after deployment

Latency P50/P75/P95 for key assets
Cache hit ratio and origin request reduction
Bandwidth egress per day / month
CPU and power profile during peak traffic

Tip: Track savings in dollars per GB saved. Even small PoPs often pay for themselves within months versus cloud egress for high-ogress assets like maps, firmware, or media.

Checklist: deploy a Pi CDN PoP in 7 steps

Provision Pi nodes and install 64-bit minimal OS.
Install k3s and join agents.
Deploy MetalLB and an Ingress (Caddy or Nginx).
Deploy Nginx caching DaemonSet + persistent volume for cache.
Deploy one inference service on AI HAT+ node with a local model and caching layer.
Implement invalidation (surrogate-key + pub/sub) and CI/CD purge hooks.
Instrument with Prometheus, run benchmarks, and iterate on cache sizing.

Final thoughts: the right tool for the right job

By 2026, combining Raspberry Pi 5 clusters with AI HAT+ units creates a new option for teams who need localized, low-cost caching and lightweight inference. They are not a full replacement for global CDNs, but as complementary PoPs they deliver measurable improvements in latency, bandwidth costs, and privacy. For technology professionals, the value lies in designing PoPs as part of a multi-tier caching strategy—automated, observable, and orchestrated.

Actionable next steps

Prototype a single Pi 5 PoP with Nginx and a small static site. Track hit ratio and bandwidth after 2 weeks.
Deploy a lightweight inference API on AI HAT+ and cache responses by prompt hash. Measure latency and CPU utilization.
Integrate an invalidation webhook into your CI/CD pipeline to purge caches on deploys.

Ready to build a low-cost CDN PoP? Start with a 3-node Pi 5 cluster and a simple Nginx cache in the next 48 hours — measure results, then scale policies for model-serving and advanced invalidation.

Resources & further reading

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