Automotive & robotics · use case
The shop-floor AI that doesn’t phone home
“Line 3 is running hot. The technician opens five tools, reads three SOPs, calls the retired specialist, and checks the historian. We have one process, six interfaces, zero memory.”
Automotive OEMs, tier-one suppliers, and robotics integrators share a compact AI wish-list: an assistant on the plant floor that can read PLCs and SCADA, a test-bench corpus an engineer can query, a pattern memory that survives model-year boundaries, and a strict posture that IP stays inside the facility perimeter.
Eldric AI OS ships the relevant pieces today. See also the industrial AI ops-assistant article for the broader positioning.
Value propositions
Plant-floor protocols native
The iot module speaks OPC-UA, Modbus TCP/RTU, and MQTT Sparkplug B directly — no SCADA-to-REST middleware. Live tag values enter the fan-out as citeable sources.
Store-and-forward reliability
WAN flap? Buffer drains on reconnect. Cell-level islanding doesn’t lose telemetry. The AI still answers questions about last shift after the link was down.
ECU archive search
Decade-long .a2l / .mdf / .dbc archives live on NFS. The data module indexes them; the chat answers “which calibration hit 98% on the cold-start test?” with a pointer.
Cross-platform Matrix Memory
Per-program tenant, per-platform workgroup. Matrix Memory compresses failure patterns across model years. The ‘did we see this before’ question gets answered in milliseconds.
IATF 16949-shaped audit
Hash-chained audit log per work-order. Traceability requirements met by construction, not by bolt-on tooling.
IP stays inside the plant
On-prem llama.cpp. No supplier chat content lands in a hyperscaler’s logs. Tier-one and OEM confidentiality boundaries are tenant boundaries.
AI-driven differentiator
The industrial-AI market has been dominated by SaaS ingestion platforms that assume your PLCs can talk to their cloud. They can’t, they shouldn’t, and your safety engineer will say no. Eldric inverts the assumption: the AI runs where the PLC already is, speaks the protocol the PLC already speaks, and stays inside the plant network. That’s the shape that actually passes shop-floor IT review.
Scalable use cases
- Body shop / final assembly. Per-line tenant. Live OPC-UA + historian + SOP fan-out. First-line technicians get cited answers, not tab-switching.
- Powertrain test benches. Modbus / OPC-UA to the rig. Every run archived. Chat answers “which sweep showed the torque dip?” with plot references.
- Homologation corpora.
data.pageindexover UNECE / FMVSS / CCC regulatory text. Draft responses cited to paragraph level. - Robotics integrators. Robotics-domain Matrix Memory (128/512). Fleet-level failure patterns without per-cell retraining.
- Supplier quality. Per-supplier tenant. Chat grounded in that supplier’s PPAP + PSW + historical non-conformances. Chinese walls enforce confidentiality.
Runs on commodity hardware
Eldric AI OS was built to land on small clusters, not on hyperscaler fleets. The whole stack is one binary; the on-prem LLM is embedded llama.cpp. The hardware plan that gets most organisations into production looks like this:
3× RTX 4090 — sweet spot
72 GB total VRAM with tensor-split. Llama 3.3 70B Q4 at 60–80 tok/s, a parallel 8B routing model, and an embedding server concurrently. One-time hardware cost ~€5–7k.
Single RTX 4090 / 4080 — team scale
24 GB. Llama 3.1 8B at 80+ tok/s, 13B comfortable, 32B Q4 possible. Enough for a small department chat with fan-out retrieval.
CPU-only — pilot scale
llama.cpp on 32+ core x86 runs 8B Q4 usefully. Matrix Memory is CPU-memory-bound. A refurbished server from the rack is enough to prove the architecture.
Scale up
Multi-node cluster with H100 / GH200 for research-grade workloads. Same binary, same role modules, topology-aware. See the HPC article.
Plant edge baseline
One 3×4090 node at the plant edge runs inference + iiotd for a whole assembly line. No WAN dependency means no downtime when the MPLS link to HQ blinks.
The arithmetic: a €6k workstation displaces a €30–60k-per-year SaaS-AI contract that still leaks IP, still can’t reach your mainframe, and still has a “we may use your data for training” clause hiding somewhere.
What the disk bill looks like
| Artefact | Size | Notes |
|---|---|---|
eldric-aios-5.0.0-3.alpha3.fc43.x86_64.rpm | ~1.4 MB | CPU baseline binary; one RPM, one systemd unit. |
eldric-aios-cuda add-on | ~512 MB | Pulled in automatically via Supplements: cuda-drivers on GPU hosts. Contains GGML_CUDA llama.cpp. |
| Llama 3.1 8B Q4_K_M GGUF | ~4.9 GB | Good default for team-scale chat on a single 4090. |
| Llama 3.3 70B Q4_K_M GGUF | ~40 GB | The sweet spot for 3×4090 tensor-split. Holds a 16k context comfortably. |
| Mixtral 8x22B Q4 GGUF | ~80 GB | Tight on 3×4090; comfortable on 4×4090 or 2×H100. |
| nomic-embed-text (embedding) | ~700 MB | CPU or GPU. One per cluster; handles vector indexing. |
Matrix Memory .emm per domain | 50–500 MB | Depends on rank × dim (see memory article). chat 64/768 ~200 kB; particle_physics 512/1024 ~500 MB. |
| Vector store per 1M chunks | ~6–10 GB | Depends on embedding dim. SQLite backend; FAISS optional. |
| Hash-chained audit log | ~200 MB / 1M calls | JSONL, append-only, rotation at 500 MB files by default. |
Three reference hardware setups
| Pilot / team | Department / BU | Production / enterprise | |
|---|---|---|---|
| CPU | 1× EPYC 7313 (16c) or i9-14900K | 2× EPYC 9354 (32c each) | 2× EPYC 9654 (96c) per node |
| GPU | 1× RTX 4090 (24 GB) | 3× RTX 4090 (72 GB) | 4× H100 (320 GB) or 8× H200 |
| RAM | 128 GB DDR5 | 256 GB DDR5 ECC | 1 TB DDR5 ECC per node |
| Storage | 2× 4 TB NVMe (RAID-1) | 6× 8 TB NVMe (RAID-10) + SSD cache | Tiered: NVMe hot + TB-scale HDD / Lustre |
| Network | 1 GbE OK | 10 GbE with link agg | 25/100 GbE or IB-HDR for multi-node |
| Power | ~1 kW typical / 1.5 kW peak | ~2 kW typical / 3 kW peak | 4–6 kW per node |
| Hardware cost | ~€4–5k | ~€12–15k | €80–250k per node |
| Serves | 8B model, 10–30 concurrent chat users | 70B Q4 at 60–80 tok/s, 200–500 users | Mixtral / Llama-405B, 2k+ users per node |
Network + ops footprint
- Ports. One outward port (443 at the edge). Internally: controller on 8880, data on 8892, inference on 8883, science on 8897, etc. — all behind the edge.
- Storage layout.
${ELDRIC_DATA_DIR}defaults to/data/eldricif writable, else/var/lib/eldric. Subdirs:models/,vectors/,memory/(matrix memory),storage/(file storage),agent/,edge/, and per-module dirs. - Backup. The audit log and
.emmfiles are the two artefacts that matter. Everything else regenerates. Snapshot the data dir nightly; off-site every week. - Updates.
dnf upgrade eldric-aios. Rollback isdnf downgrade. Zero vendor dance. - Ops team. A single systems engineer can run a pilot install. A team of two runs a department deployment. Production enterprise uses your existing Linux sysadmin rota.
SWOT — an honest read
Strengths
- iiotd module speaks OPC-UA + Modbus + MQTT Sparkplug B natively
- Matrix Memory robotics domain sized for motor-control patterns (128/512)
- Hash-chained audit log supports IATF 16949 traceability
- Single-RPM install surface — plant IT review-friendly
Weaknesses
- MES / ERP vendor-specific connectors (SAP ME, Opcenter, Oracle MES) require custom extensions
- Vision-model integration for visual inspection is customer’s choice of model
- No native integration with simulation environments (CarMaker, IPG) yet
- Certified industrial-safety posture not yet audited
Opportunities
- EU industrial sovereignty push (Chips Act, Net-Zero Industry Act)
- IATF 16949 revisions increasing traceability requirements
- Cobot + collaborative-robotics scaling in EU manufacturing
- Plant-floor WAN failures making cloud-AI intolerable
Threats
- Siemens AI embedded in TIA Portal
- Rockwell FactoryTalk AI
- PTC ThingWorx AI
- GE Digital Proficy AI
First entry points — concrete value in 30 / 90 / 180 days
One-cell pilot
Install on a workstation inside the plant network. Connect to one OPC-UA server + one NFS SOP share. Pilot with one shift of one line.
Program-wide deployment
Tenant = powertrain program. ECU archive indexed. Matrix Memory seeded with prior-year test-bench data. IATF 16949 audit log reviewed by QA.
Multi-plant rollout
Per-plant tenant, shared group memory. Supplier chinese walls enforced. Cross-program failure-pattern queries quarterly.