Benchmark: Redis on Tiny Devices — Performance of Caches on Raspberry Pi 5 vs Desktop Servers

Hook — Why this matters for your edge AI deployments

If you operate AI at the edge, caching is now a gating factor for user-perceived latency and bandwidth costs. Small devices like the Raspberry Pi 5 (when paired with the new AI HAT+) can run local inference and caches, but they behave very differently than x86 servers when you enable persistence. This benchmark-driven guide gives technology teams the data and configuration patterns you need to choose the right Redis strategy for edge AI in 2026.

Summary — Key findings up front (inverted pyramid)

• Raw speed: A modern x86 desktop/server (NVMe, many cores) outperforms a Raspberry Pi 5 by ~4–6x on small-key SET/GET workloads measured as ops/sec.
• Persistence costs: Enabling persistence (AOF with fsync=always or aggressive RDB snapshots) has a disproportionately larger throughput and latency penalty on Pi 5 because of slower I/O and fork overhead on constrained RAM.
• Best edge pattern: For sub-10ms p95 on local inference caches, prefer memory-only Redis with asynchronous durability (replicate to a more powerful node or use periodic bulk sync) or use AOF with appendfsync everysec only if you have NVMe and reliable power.
• Observability: Use Redis latency tools + Prometheus Redis exporters + OS-level tracing (fork latency, fsync) to correlate p95 spikes during persistence events.

Why test Raspberry Pi 5 + AI HAT+ vs x86 in 2026?

Two trends make this comparison essential in 2026: (1) the proliferation of small AI accelerators (AI HAT-style NPUs and on-device quantized models) pushing inference to the edge, and (2) the maturation of server-class RISC-V, NVLink and energy-efficient x86 platforms used in micro-datacenters. Edge nodes are expected to serve both model inference and fast caches for embeddings, session state, and feature descriptors. Redis remains the de-facto cache for those workloads — but the tradeoffs between memory-only and persistent configurations are amplified on tiny devices.

Benchmarks — methodology and environment

Testbed (representative)

• Raspberry Pi 5 with AI HAT+ (latest firmware, 8 GB RAM model), running 64-bit Linux, storage: SD card for baseline; NVMe via adapter for a subset of tests.
• x86 Desktop Server: 16 logical cores (8 cores/16 threads), 64 GB RAM, NVMe SSD, Linux.
• Redis OSS 7.x compiled with default settings.

Workload

• Payloads: 64B (small), 512B (typical embedding descriptors), and 4KB (larger artifacts).
• Tools: redis-benchmark and memtier_benchmark for throughput; Prometheus Redis exporter + Grafana for time-series; OS tools: vmstat, iostat, perf to capture fork/fsync behavior.
• Client configuration: 50 concurrent clients, 4 threads, 100k total ops per test. Tests repeated 5x and median reported.

Persistence modes tested

1. Memory-only: persistence disabled (no RDB, no AOF).
2. RDB snapshots: default snapshot intervals; forced BGSAVE during test to measure fork impact.
3. AOF everysec: appendonly yes, appendfsync everysec.
4. AOF fsync=always: appendonly yes, appendfsync always (worst-case durability).

Representative results (median values)

Numbers below are from our testbed and should be used as directional guidance. Your exact metrics will vary by NIC, storage media, kernel version and OS tuning. All times are p95 latencies unless otherwise noted.

64B payload (SET/GET mix)

• Server, memory-only: ~420k ops/sec, p95 ~0.8 ms
• Pi 5, memory-only: ~85k ops/sec, p95 ~3.2 ms
• Server, AOF everysec: ~370k ops/sec (-12%), p95 ~1.1 ms
• Pi 5, AOF everysec (SD): ~65k ops/sec (-24%), p95 ~5–8 ms
• Pi 5, AOF everysec (NVMe): ~72k ops/sec (-15%), p95 ~4 ms
• Pi 5, AOF fsync=always: ~25k ops/sec (-70%), p95 20–60 ms (spiky)

512B payload (embeddings-style)

• Server, memory-only: ~230k ops/sec, p95 ~1.2 ms
• Pi 5, memory-only: ~45k ops/sec, p95 ~4.5 ms
• Server, AOF everysec: ~200k ops/sec (-13%), p95 ~1.6 ms
• Pi 5, AOF everysec (NVMe): ~36k ops/sec (-20%), p95 ~7.5 ms

Observations from the data

• Absolute throughput gap: Small-key workloads emphasize CPU and single-core IPC. The server's higher frequency and memory bandwidth deliver 4–6x raw throughput.
• Persistence disproportionately hurts Pi: The Pi's storage and the cost of background fork (copy-on-write) combined with less headroom in RAM create higher p95 spikes when RDB/AOF activity occurs.
• NVMe matters: When the Pi uses NVMe (via a HAT or adapter), throughput and latency improve for AOF workloads; still, CPU-bound throughput does not catch up to x86 servers.

Why persistence penalties are worse on tiny devices

Redis persistence uses two mechanisms that interact poorly with constrained systems: background fork (RDB) and fsync latency for AOF. On a low-memory device the fork can cause heavy copy-on-write page churn, leading to high latencies and increased memory usage. Likewise, fsync waits on slow SD or USB-backed storage block other I/O and increase request latency, especially when set to always.

Practical configuration snippets and system tuning

Below are recommended redis.conf and OS-level tweaks to squeeze predictable latency out of Pi-class hardware without risking data loss beyond your acceptable threshold.

redis.conf suggestions (edge-focused)

# Memory-first configuration for edge caches
maxmemory 6gb
maxmemory-policy volatile-lru
# Disable automatic RDB snapshots for memory-first workloads
save ""
# Use AOF with relaxed fsync if NVMe is present
appendonly yes
appendfsync everysec
# Do not stop serving if RDB/AOF has errors (careful with durability)
stop-writes-on-bgsave-error no
# Tune client limits
maxclients 10000
tcp-backlog 511

OS-level kernel tweaks (run as root or via sysctl)

sysctl -w vm.overcommit_memory=1
sysctl -w vm.swappiness=10
# Disable transparent hugepages for more predictable latency
echo never > /sys/kernel/mm/transparent_hugepage/enabled
# Increase max open files if needed
sysctl -w fs.file-max=200000

Storage recommendations

• Avoid using slow SD cards for AOF or heavy writes. Prefer NVMe via adapter or a high-endurance USB SSD.
• Use zram for swap to avoid SD wear and reduce I/O jitter.
• When durability is required but power is unreliable, push AOF to a remote replica or central store asynchronously rather than fsync=always on the Pi.

Benchmark commands you can run

# Small payload test using redis-benchmark
redis-benchmark -h 127.0.0.1 -p 6379 -c 50 -n 100000 -d 64 -t set,get

# Using memtier_benchmark for richer metrics
memtier_benchmark -s 127.0.0.1 -p 6379 -t 4 -c 50 -n 100000 --ratio 1:1 --data-size=64

Monitoring and debugging recommendations

Observability is critical to reconcile cache behavior with CI/CD rollouts and model updates. Track both Redis metrics and OS events that produce the spikes described earlier.

Key Redis metrics

• ops/sec (cmdstat.*)
• instantaneous_ops_per_sec
• used_memory vs maxmemory
• keyspace hits/misses
• persistence counters: rdb_bgsave_in_progress, aof_current_size, aof_last_fsync_usec
• latency: use LATENCY LATEST and SLOWLOG

OS-level signals to collect

• fork time and page-faults (perf or eBPF probes around fork/clone during BGSAVE)
• fsync latency distribution (use blktrace or iostat -x)
• CPU steal/irq and run queue length (top, vmstat)

Practical observability snippet

# Capture fsync latency spikes with bpftrace (example)
bpftrace -e 'tracepoint:syscalls:sys_enter_fsync { @ts[tid] = nsecs; } tracepoint:syscalls:sys_exit_fsync / @ts[tid] / { printf("fsync %d took %d ms\n", tid, (nsecs - @ts[tid]) / 1000000); delete(@ts[tid]); }'

Edge AI caching patterns: when to choose what

Below are pragmatic patterns for common edge AI caching needs.

1) Ultra-low-latency inference cache (p95 < 10 ms)

• Use: memory-only Redis on the Pi; keep entries small, TTLs short, and use eviction policies.
• Durability: asynchronous — replicate critical changes to a more powerful hub node or batch-export snapshots during low-load windows.

2) Durable session/feature store on constrained edge

• Use: AOF with appendfsync everysec and NVMe on-device. If NVMe not available, prefer replication to remote or periodic rsync of RDB to persistent store.
• Tradeoffs: expect 10–30% throughput loss vs memory-only; watch p95 for fsync-induced spikes.

3) Large embedding caches

• Embeddings blow up RAM. Use compressed/quantized vectors (int8/4-bit) and a hybrid approach: keep hot set in memory, cold set on local SSD and retrieve into memory on demand.
• Consider a tiered cache: a tiny Redis on Pi for hot keys and a remote Redis/DB for cold keys. Use consistent TTLs and background prefetching after model updates.

2026 trends that change the calculus

Two technology trends are worth watching as they influence the edge caching decision in 2026.

• Edge NPUs and faster local NVMe: AI HAT-style NPUs are common and some HATs now expose PCIe/NVMe, lowering the cost of durable AOF on-device. If your Pi is NVMe-backed, the gap to x86 for persistence workloads narrows significantly. See notes on micro-edge VPS trajectories.
• Compute heterogeneity (RISC-V & NVLink fusion): emerging silicon (RISC-V with NVLink-like fabrics) will enable denser edge nodes where a small accelerator plus a low-power host CPU blurs the line between tiny devices and micro-servers for caching patterns.

Case study (short): Edge conversational agent

We deployed a conversational assistant on 20 Pi 5 nodes paired with AI HAT+ units, caching session contexts and small embedding states in Redis. Initial deployment used AOF fsync=always and suffered p95 spikes up to 200 ms during periods of high write volume. After changes — switching to memory-only for session reads, AOF everysec for write-behind of session checkpoints, and NVMe for nodes that required durability — user-perceived latency dropped 3x and network egress costs were cut by 40% because repeated fetches to the cloud were avoided. For deployment tooling and example field kits, teams may find our notes on edge field kits for cloud gaming useful for hardware-level thinking.

Checklist — What to measure before you decide

1. Profile your workload (payload size, read/write ratio, TTL churn).
2. Measure p95 and p99 latencies, not just average ops/sec.
3. Test persistence modes on the actual hardware and media (SD vs NVMe).
4. Simulate background persistence (BGSAVE, AOF rewrite) while measuring tail latency.
5. Monitor fork and fsync latency with kernel-level tracing.

Actionable takeaways

• For low-latency edge inference: favor memory-only Redis on Pi 5 and perform durability asynchronously at the hub — this minimizes p95 spikes.
• If you need strong on-device durability: use NVMe-backed storage and appendfsync everysec; avoid fsync=always on SD-based devices.
• Measure tail latency: instrument for p95/p99 and trace fsync/fork events — those are the real killers.
• Consider hybrid architectures: hot in-memory cache on Pi, durable store off-device or on a stronger local node; replicate selectively. For cloud tie-ins and case studies on developer workflows, see Bitbox.Cloud case studies.

Final notes and caveats

Benchmarks are sensitive to kernel version, storage, and compile-time flags. Use these numbers as a starting point and reproduce the tests against your own models and client patterns. The core insight remains stable in 2026: persistence changes the game more on tiny devices than on servers, and NVMe + OS tuning narrows but does not eliminate the gap.

Call to action

Ready to benchmark your own edge nodes? Download our reproducible benchmark scripts and Prometheus dashboards (includes memtier and redis-benchmark recipes, and bpftrace snippets) at caching.website/edge-redis-bench. Run the suite on your Pi 5 + AI HAT+ and compare results with your x86 nodes — then share the output and we’ll help interpret the results and recommend configuration changes tailored to your workload. You can also mirror scripts and dashboards into a JAMstack repo for reproducible runs via Compose.page.

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