Edge-Native Model Stores: Caching Model Artifacts for Distributed RISC-V+GPU Inference

Edge-Native Model Stores: Caching Model Artifacts for Distributed RISC-V+GPU Inference

Hook: If your inference fleet struggles with long cold-starts, unpredictable bandwidth bills, or inconsistent model freshness across distributed RISC-V nodes and NVLink-connected GPUs, this design guide gives you a production-ready pattern to fix it. We architect a distributed model store that treats model artifacts as first-class cached objects: local caches on RISC-V edge nodes, NVLink-aware shard placement, and eviction tuned to inference hotness.

Quick summary (most important first)

In 2026, the emergence of NVLink Fusion on RISC-V silicon and the proliferation of low-power AI HATs for devices like Raspberry Pi make it practical to push real inference workloads to the edge. To do this reliably you need a distributed model store with:

• Local caches on RISC-V nodes that serve artifact reads and pre-warm GPU memory over NVLink.
• Shard placement algorithms aware of NVLink topology and GPU memory constraints.
• Eviction and prefetch policies driven by inference hotness (QPS, reuse window, cost-to-load).
• Clear origin/edge/browser semantics for consistency, plus CI/CD hooks to manage releases.

Why this matters in 2026

Late 2025 and early 2026 brought two trends that change the caching calculus for model artifacts:

• SiFive and other RISC-V vendors announced NVLink Fusion partnerships enabling low-latency links between RISC-V hosts and Nvidia GPUs. This creates new topologies where local CPU-to-GPU links are much faster and more deterministic than traditional PCIe over x86 servers.
• Edge AI hardware (e.g., Raspberry Pi AI HATs and small form-factor RISC-V boards) grew more capable, making distributed inference economically feasible at the edge.

"NVLink Fusion on RISC-V removes a key bottleneck: the CPU-to-GPU link. Use it to treat GPU memory as an extension of local cache layers — if you architect carefully." — Infrastructure engineering takeaway, 2026

Design goals and constraints

Design a model store that:

• Minimizes tail latency for inference.
• Reduces bandwidth and origin load (CDN/OSS egress costs).
• Ensures predictable freshness when models are promoted or rolled back.
• Operates on heterogeneous hardware: RISC-V nodes with NVLink-connected GPUs, lightweight ARM nodes, and central origins.

Architecture overview

High-level components:

1. Origin model repository — canonical artifacts, immutable releases (S3/OCI registry).
2. Regional edge caches — Kubernetes clusters or appliances close to end devices; hold full model files and serve shards to nodes.
3. Node-local cache on RISC-V machines — small, fast storage (NVMe/memory) to host shards and prefetch into GPU over NVLink.
4. GPU runtime cache — shards resident in GPU memory or in shared NVLink pool (for multi-GPU NVSwitch topologies).
5. Controller/placement service — decides which shards go to which node/GPU given topology, hotness, and memory constraints.

Data plane vs. control plane

Keep the dataplane simple: pull shards via HTTP/QUIC from edge caches; GPU loaders map via DMA/NVLink. The control plane runs the placement and eviction logic, metrics aggregation, and CI/CD hooks for model promotions.

Edge, origin, browser: a caching continuum

Think of the model artifact lifecycle the same way you treat web cache layers:

• Browser (device): client-side micro-caches for tiny artifacts such as tokenizer configs, light metadata, or quantization tables. Important when inference originates from user devices.
• Edge cache: regional caches that store models and serve shards to nearby nodes. They absorb egress and reduce cold starts for nodes beginning to warm GPUs.
• Origin: immutable storage for releases and legal provenance. Origin responds to misses and is the source of truth for pushes and rollbacks.

Treat model artifacts using HTTP caching semantics (ETags, Range requests for shard serving) and sign artifacts for integrity. Adapt browser/edge/origin TTLs: browser-level TTLs are short for metadata; edge TTLs are longer but controlled by CI/CD release flow.

Shard placement: topology-aware strategies

Shards are units of storage (e.g., quantized tensor slices, layer partitions, or fused attention blocks). Placement must minimize transfer time and maximize reuse.

Key signals for placement

• NVLink topology: which RISC-V nodes connect to which GPUs and whether NVSwitch exposes a shared pool.
• GPU memory and free capacity per GPU.
• Inference hotness for model + shard (recent QPS, reuse-window length).
• Shard size and cost-to-load (time to transfer/shard decode).

Placement algorithm (practical outline)

Use a two-phase approach:

1. Global planner: runs less frequently (minutes). Computes an ideal shard-to-GPU assignment using a constrained optimization: maximize expected saved load time subject to GPU capacity and NVLink affinity. Solve with a greedy knapsack or ILP for small clusters.
2. Local allocator: reactive, node-level decision when a shard is requested and not present. It decides whether to fetch to local NVMe, prefetch to GPU, or stream on-the-fly via NVLink from the edge cache.

Greedy placement pseudocode

// inputs: shards S (size, hotness), GPUs G (capacity, nvlinkAffinity)
sort S by score = hotness / size descending
for shard in S:
  for gpu in G sorted by nvlinkAffinity(shard.originNode, gpu) descending:
    if gpu.free >= shard.size:
      assign(shard,gpu)
      gpu.free -= shard.size
      break

The nvlinkAffinity function favors GPUs directly attached to the requesting RISC-V node or reachable via NVSwitch with low hop count.

Eviction, hotness, and prefetch

Eviction is the critical lever for predictable performance. Classic LRU isn't enough — you need a cost-aware strategy.

Hotness-driven eviction

Compute a per-shard utility score combining:

• QPS (recent requests per minute)
• Reuse window (time between first and last request in a sample period)
• Load cost (time to fetch from edge/origin; higher if origin is remote or egress-costly)
• Memory pressure (how urgently we need space)

Utility(shard) = (alpha * normalizedHotness) + (beta * reuseWindow) + (gamma * loadCost). Evict shards with lowest utility first. Tune alpha/beta/gamma per workload.

Prefetch policies

• Rule-based prefetch: if a shard's QPS crosses a threshold, prefetch remaining shards for that model to the same GPU ahead of time.
• Windowed lookahead: on model A hitting 80% of capacity, prefetch shards for the top-K sibling models the controller expects next (based on session traces).
• Cold-start prewarming via CI/CD: when a model is promoted, the pipeline triggers regional prewarming to populate edge caches and a fraction of nodes.

Local cache layout and NVLink flow

On the RISC-V node, implement a two-tier local cache:

1. Fast tier — in-memory or tmpfs, used as staging to map shards into GPU over NVLink.
2. Persistent tier — NVMe or eMMC to store evicted shards to avoid re-downloading from edge origin on the next warm-up.

When inference needs a shard:

1. Check GPU resident set (direct map).
2. If missing, check fast tier; if present, DMA via NVLink into GPU memory (low latency).
3. If not present, fetch from persistent tier; if not present, pull from regional edge cache.
4. Optionally stream decode on-the-fly for very large shards, but prefer prefetch for latency-critical paths.

Sample local loader (conceptual)

async function ensureShardPresent(shardId):
  if gpu.has(shardId): return
  if fastTier.has(shardId): dmaToGpu(fastTier.path(shardId)); return
  if persistentTier.has(shardId): copyToFast(persistentTier.path(shardId)); dmaToGpu(...); return
  // miss: fetch from edge
  data = await httpGet(edgeUrl + shardId)
  write(persistentTier, shardId, data)
  copyToFast(...)
  dmaToGpu(...)

Consistency, TTLs and CI/CD integration

Models must be immutable releases (v1.2.0-202601). For correctness:

• Use content-addressed artifact names (SHA256) to avoid in-place mutations.
• Serve with strong ETags and signed manifests for authenticity.
• Promote models through environments with controlled prewarming: staging -> regional prewarm -> global rollout.
• Support rapid rollback by flipping pointer manifests; edge caches honor short negative cache TTLs so rollbacks are fast.

Observability: the metrics you need

Track these per-model and per-shard metrics:

• Shard hit rate at GPU, node fast tier, persistent tier, edge cache.
• Load cost (ms) from each tier.
• Eviction events and utility at time of eviction.
• End-to-end tail latency for inference (p95/p99) correlated to shard misses.
• Egress saved and estimated cost savings.

Expose these to your controller; use them in placement re-computation and to tune alpha/beta/gamma for eviction utility.

Security and provenance

Sign all shards and manifests. Enforce transport security (mTLS/QUIC). Restrict which nodes can request model promotions and prune older artifacts with attestation records to track which nodes ran which versions.

Benchmarks & expected gains (practical guidance)

Benchmarks will vary, but use this baseline to set expectations and measure improvements in your environment.

Example microbenchmark (realistic expectations)

Test setup: 7B quantized model split into 32 shards. RISC-V node with NVLink Fusion attached to an Nvidia Ampere/Blackwell GPU. Edge cache in same region (10ms RTT).

• Cold load from origin (no edge cache): 1–3s to load shards to GPU — high cost and long tail latency.
• Edge cache miss but local persistent tier hit: ~200–400ms to prefetch and map relevant shards into GPU.
• Fast-tier + NVLink present (prefetched): 10–60ms to DMA shards into GPU memory, reducing p95 inference tail significantly.

In practice, moving from origin-first loads to NVLink-enabled local prefetch often reduces cold-start latency by an order of magnitude and lowers egress from origin by 5x–20x depending on reuse patterns. Measure your workload to tune thresholds.

Operational recipes (step-by-step)

1) Build artifacts as sharded, quantized and content-addressed files

• Export sharded weights in a standard format (e.g., fused .npz or flatbuffers), quantize where appropriate.
• Record shard sizes, checksums, and layer boundaries in manifest.json (signed).

2) CI/CD: promote with prewarm hooks

1. On release, upload shards to origin and push manifest to registry.
2. Trigger regional prewarm jobs that populate edge caches.
3. Optionally run a progressive rollout: warm 5% of nodes, measure, then increase.

3) Controller: run placement every N minutes

• Gather metrics, compute utility, run greedy placement, and issue placement decisions as watch events to nodes.
• Nodes reconcile: pull shards to persistent tier and optionally prefetch to fast tier.

4) Runtime: local loader and eviction

• Local loader responds to requests and updates per-shard hotness counters.
• Eviction runs at fixed intervals or on memory pressure and uses the utility score to evict.

Integration with Kubernetes and scheduling

Use custom schedulers or node selectors to keep RISC-V control plane pods close to NVLink GPUs. Use device plugins to expose NVLink topology to the controller. Annotate nodes with NVLink groups so placement can prefer GPUs in the same NVSwitch domain.

Failure modes and mitigations

• Edge cache outage: allow direct origin fallback but throttle to avoid spikes; maintain local persistent tier to serve short future requests.
• GPU OOM: fail fast, degrade to CPU inference or smaller model shards; use autoscaler to spin additional NVLink-capable nodes if available.
• Stale manifests: use signed manifests and short negative TTLs to recover quickly on rollback.

Future predictions (2026 and beyond)

Expect these trends to shape implementations:

• RISC-V + NVLink will become an accepted edge inference platform for medium-sized models (3B–20B) by late 2026.
• Shared NVLink pools (NVSwitch-like fabrics) will encourage model-serving patterns that treat GPU RAM as a distributed cache pool, not strictly per-node.
• Model stores will standardize on shard manifests with cost metadata to allow smarter cross-vendor placement logic.

Actionable takeaways

• Start sharding now. Even coarse-grained shards cut cold-starts and enable smart placement.
• Measure hotness. Capture QPS, reuse windows and load cost and fold them into eviction utility scores.
• Exploit NVLink affinity. Place shards on GPUs directly attached to RISC-V nodes first.
• Use CI/CD prewarm. Automate regional prewarming as part of promotion pipelines to avoid user-impactful cold starts.
• Observe aggressively. Correlate shard miss events to tail latency and cost; adjust thresholds with A/B testing.

Example YAML: controller placement config

placement:
  interval: 60s
  shardScore:
    hotnessWeight: 0.6
    reuseWindowWeight: 0.3
    loadCostWeight: 0.1
  nvlinkAffinityBias: 2.0
  prefetch:
    qpsThreshold: 5
    lookaheadModels: 3

Closing: put model artifacts on a cache-first path

In a world where RISC-V hosts use NVLink to unlock fast CPU-to-GPU transfers, treating model artifacts as cacheable, shardable objects transforms operational economics and latency. The right mix of local caching, topology-aware placement, and hotness-driven eviction will cut tail latency, reduce egress costs, and make progressive rollouts predictable.

Call-to-action: Start by sharding one production model, add per-shard hotness metrics, and run the greedy placement loop for a week. If you want a reference implementation, benchmark scripts, and a starter controller for RISC-V + NVLink fleets, visit cached.space/modelstore to clone the repo and try a lab deployment.

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