The model does not fit
Teams cut model quality, shorten context or upgrade the entire device because the runtime treats memory as a static allocation.
Private beta · Design partners
Run larger AI models on the hardware you already ship.
InfernoRT is the memory-native inference runtime for robotics, industrial edge and private AI. It finds the fastest legal execution plan under a hard RAM ceiling—without sending your data to the cloud.
One target device. One model. One memory ceiling. A reproducible answer in 4–6 weeks.
LGI execution plan
ADMITTED
MODEL
Meta Llama 3.1 8B
RAM CEILING
4.00 GB
Hot
Warm
Cold / staged
Guard band
01AdmitProve the cap
02PlanPlace memory
03ExecuteMove only what is needed
8B / 4 GBWorking constrained-memory runtime
90.06 tok/sValidated 1B resident median
19 artifactsFully valid calculator evidence
Hard RAM capsAdmission before execution
Performance depends on model, quantization, context, storage and hardware. Every number below is configuration-specific and backed by a fully valid production-harness artifact.
ROBOTICSINDUSTRIAL EDGEDEFENCE & SOVEREIGNPRIVATE ENTERPRISE AIEDGE APPLIANCES
THE PROBLEM
Compute scaled. Memory didn’t.
Most inference stacks assume abundant RAM, expensive accelerators and a dependable cloud connection. Real devices have fixed memory, power and thermal limits.
The cloud breaks the product
Cloud inference adds variable cost, latency, connectivity risk and a new data boundary—often exactly where autonomy matters most.
“It ran once” is not a guarantee
Average memory usage is not a deployment contract. A production runtime must prove the requested workload stays inside the cap.
THE RUNTIME
Memory becomes a managed system.
InfernoRT plans model residency, KV state, staging and execution together. The goal is not “use less memory” in the abstract—it is to find the fastest legal plan for the exact hardware limit.
01 · ADMISSION
Prove the workload before it runs
The admission solver evaluates model, context, KV state, staging, scratch and guard bands before execution begins.
- Explicit RAM ceiling
- Plan-specific memory proof
- Predictable rejection instead of surprise OOM
02 · RESIDENCY
Keep the right bytes hot
Weights and KV state move through hot, warm, cold and evicted residency according to the active execution plan.
03 · EXECUTION
Move less. Reuse more.
Segmented execution reduces duplicate live memory and limits data movement to the working set needed for each phase.
04 · TELEMETRY
Know what the runtime actually did
Expose selected plan, physical peak, residency, stage traffic and contract failures—not just a single tokens-per-second number.
| Deployment question | Conventional runtime | InfernoRT |
|---|---|---|
| Will this workload fit? | Try it and monitor | Admit against a hard cap |
| What happens under pressure? | OOM, swap or manual tuning | Switch to a legal residency plan |
| Must the whole model stay resident? | Usually | No—residency is planned |
| Can the device work offline? | Only if the model already fits | Designed for local constrained execution |
| Can we audit memory behaviour? | Limited | Plan and residency telemetry |
INTERACTIVE PLANNER
Put your own numbers in.
First check whether the model fits your current hardware. Then estimate the fleet value of avoiding a more expensive device configuration.
CANDIDATECalibrated estimate · conservative range
InfernoRT candidate under a 4 GiB ceiling
The model is larger than a conventional fully resident working set. InfernoRT would evaluate a segmented residency plan and validate it on the target device.
Approx. model file5.00 GiB
Native working set6.24 GiB
Likely planSTREAMED SEGMENTS
Estimated decode speedCalculating…
Measured configurations remain exact anchors; other results are conservative calibrated estimates.
FLEET CASESimple deployment economics
InfernoRT pays back at 120 devices
Hardware spend avoided$750,000
InfernoRT licences$500,000
Break-even fleet size120 devices
Estimated fleet saving$247,000
Fleet saving = devices × (hardware upgrade avoided − InfernoRT licence) − pilot and integration. It excludes financing, tax, support and product revenue.
MEASURED, NOT MAGICAL
Claims should survive a harness.
InfernoRT’s benchmark program tracks semantic correctness, format compliance, physical memory, admission plan, residency and throughput together. A fast wrong answer is not a win.
Llama 3.2 1B · Q4_K_M · 4 GB
90.06 tok/s medianShort 389-token profile, 17-token cap, full-resident plan. Output quality, answer format, memory limit and runtime integrity checks passed.
Llama 3.2 3B · Q4_K_M · 4 GB
12.55 tok/s medianLong 1,733-token profile, 37-token cap, evicted plan. All quality and execution contracts passed.
Llama 3 8B · Q4_K_M · 8 GB
14.49 tok/s medianMedium 877-token profile, 131-token cap, full-resident plan. All quality and execution contracts passed.
Llama 3 8B · Q4_K_M · 4 GB
5.45 tok/s medianLong 1,733-token profile, 257-token cap, evicted plan. Peak physical footprint stayed at 2.40–2.41 GiB.
MATCHED CPU CONTROL
One chart. Every supported model. The measured truth.
Same GGUF, prompt, greedy sampling, context, output cap, eight CPU threads and RAM ceiling. Wins, losses and constrained-memory results are all shown.
1.34×best measured speed advantage
5 model classesmeasured, not estimated
8B + 70Brun where the control exceeds the cap
Llama 3.2 1B4 GiB · ctx 877 · out 131+29.1%
InfernoRT
llama.cpp
Llama 3.2 3B8 GiB · ctx 877 · out 131+25.3%
InfernoRT
llama.cpp
Llama 3 8B8 GiB · ctx 877 · out 131+11.7%
InfernoRT
llama.cpp
Llama 3.1 8B8 GiB · ctx 877 · out 131+34.4%
InfernoRT
llama.cpp
Llama 3 8B · constrained4 GiB · ctx 1,733 · out 257Runs at 4 GiB
InfernoRT
llama.cpp
OOMNo valid result under the 4 GiB cap
Llama 3.1 70B · constrained20 GiB · ctx 113 · out 13Runs at 20 GiB
InfernoRT
0.0129 TPS
llama.cpp
OOMExceeded the 20 GiB cap
| Model | RAM | Context / output | InfernoRT TPS | llama.cpp TPS | Difference |
|---|---|---|---|---|---|
| Llama 3.2 1B · Q4_K_M | 4 GiB | 877 / 131 | 71.56 | 55.45 | 1.29× faster |
| Llama 3.2 3B · Q4_K_M | 8 GiB | 877 / 131 | 42.54 | 33.96 | 1.25× faster |
| Llama 3 8B · Q4_K_M | 8 GiB | 877 / 131 | 16.04 | 14.36 | 1.12× faster |
| Llama 3.1 8B · Q4_K_M | 8 GiB | 877 / 131 | 16.96 | 12.62 | 1.34× faster |
| Llama 3 8B · Q4_K_M | 4 GiB | 1,733 / 257 | 5.45 | OOM | InfernoRT runs |
| Llama 3.1 70B · Q4_K_M | 20 GiB | 113 / 13 | 0.0129 | OOM | InfernoRT runs |
The 1B row is one matched measurement. Direct 3B and resident 8B rows use three-run medians after a warm-up. Constrained 8B uses a three-run InfernoRT median; constrained 70B is one exact-answer capability run. The 70B control exceeded 20 GiB before generating a token. Incorrect or badly formatted answers never contribute speed.
19 valid artifactsOnly contract-clean production runs calibrate the public estimator
Correct answers onlyIncorrect or badly formatted answers receive zero usable throughput
Physical-cap monitoredPeak physical footprint is checked against the requested RAM budget
Measured-first calculatorExact rows are measured; interpolation and structural estimates are labeled
WHERE IT MATTERS
AI that still works when the cloud is unavailable.
The strongest fit is a device or private deployment with valuable local data, a fixed hardware envelope and a model that is currently too expensive—or impossible—to run locally.
Robotics & autonomous systems
Local reasoning, planning and operator assistance without round trips to a remote API.
Industrial edge
Maintenance, troubleshooting and process intelligence inside fixed, long-lived hardware.
Defence & sovereign AI
Private inference where connectivity, jurisdiction and data exposure are mission constraints.
Private enterprise appliances
Ship useful language-model capability on-prem without provisioning a full GPU server for every site.
DESIGN-PARTNER PILOT
Bring one painful workload.
We will test the exact model, device and RAM ceiling you care about. The outcome is a benchmark-backed go/no-go decision and a deployment path—not a generic demo.
Week 1Baseline
Lock device, model, workload, success criteria and current runtime.
Weeks 2–3Integrate
Build the legal execution envelope and instrument memory behaviour.
Weeks 4–5Validate
Compare throughput, quality, peak memory and failure behaviour.
Week 6Decision
Deliver the benchmark pack, integration plan and commercial proposal.
COMMERCIAL MODEL
Start with proof. Scale by device.
InfernoRT is sold around deployment value: hardware avoided, cloud cost displaced, privacy retained and capability unlocked.
EVALUATION
Fit assessment
Free
Initial model/device review and benchmark design.
- 30-minute technical intake
- Feasibility envelope
- Pilot recommendation
BEST START
DESIGN PARTNER
4–6 week pilot
Custom
One device, one model, one RAM target and one benchmark pack.
- Runtime integration
- Quality + memory comparison
- Deployment recommendation
OEM RUNTIME
Per-device licence
From $50/device
Volume pricing for embedded and edge products.
- Production runtime licence
- Device-class validation
- Annual support available
ENTERPRISE / SOVEREIGN
Private deployment
Custom
Annual licence, integration and regulated-environment support.
- Private distribution
- Deployment controls
- Priority engineering support
FAQ
The questions technical buyers ask first.
Is InfernoRT another model compression format?
No. InfernoRT is a runtime that plans memory and execution under a fixed device cap. Model quantization is an input to that plan, not the whole product.
Do we need to send data to InfernoRT?
No. The deployment goal is local or private inference. Pilot data-handling requirements are agreed before integration.
Does every large model become fast on tiny hardware?
No. Memory feasibility and throughput are separate questions. InfernoRT aims to make previously illegal workloads executable while selecting the fastest valid plan; the benchmark shows whether the resulting speed is useful.
What hardware is supported today?
The current development and validation focus is macOS/Apple Silicon CPU, with Linux support in progress. Windows is not currently a supported target.
Can we use our own model?
The pilot starts with the exact model and quantization you plan to ship. Current production work is concentrated on GGUF and Llama-family validation before broader model-family expansion.
What makes a strong pilot?
A fixed device, a real workload, clear success criteria, a hard RAM ceiling and an existing baseline runtime. The best pilots have a direct cloud, hardware or product-margin consequence.
MOST INTELLIGENCE. LEAST RESOURCES.
Stop redesigning the product around the runtime.
Bring the hardware you already ship and the model you actually want. InfernoRT will tell you what is legally executable—and what it takes to make it useful.