Inferno

Larger models.
Same hardware.

Your next AI feature shouldn’t need a hardware upgrade.

InfernoRT runs local language models inside a fixed memory budget. Built for the devices your business already deploys.

Check your hardware See how it works
More model. A fixed memory budget.
MODEL ON STORAGEBring in only what’s needed
4 GiBPHYSICAL RAM
CEILING
0Fixed budget
LOCAL INFERENCEYour device.
Your data.

Layers move through memory as execution advances.

Conceptual illustration · not live telemetry
8B / 4 GiB validated configurationLocal executionGGUF modelsmacOS / Apple Silicon CPU

Intelligence shouldn’t
outgrow your device.

Robotics, industrial equipment, and private infrastructure have real constraints. InfernoRT plans model residency and execution together, so you can evaluate larger models against the memory you actually have.

Keep the hardware.

Evaluate more capable models before changing your device configuration.

Plan within the limit.

Check memory feasibility before execution. Reject workloads that cannot fit the budget.

Stay local.

Run inference on the device, with no cloud connection required for execution.

A plan for every
byte in flight.

Layer-group inference coordinates what stays in memory, what moves in, and what can leave. Explore each step of the execution plan.

A memory limit is a contract.

The admission solver accounts for model weights, context, runtime state, and guard bands before choosing a legal execution plan.

Conceptual execution diagram

MEASURED BENCHMARKS

Evidence you can
build a decision on.

InfernoRT and llama.cpp, compared on matched CPU workloads. These frozen results are configuration-specific, including cases where InfernoRT is slower.

View test configuration

July 2026 evidence: same GGUF, prompt, greedy sampling, context, output cap, eight CPU threads and RAM ceiling. Incorrect or badly formatted answers do not contribute speed. The 1B comparison has one valid pair; the other speed comparisons use three measurements per runtime after warm-up.

3/3 VALID

Llama 3.2 1B · Q4_K_M · 4 GB

90.06 tok/s median
3/3 VALID

Llama 3 8B · Q4_K_M · 8 GB

14.49 tok/s median
3/3 VALID

Llama 3 8B · Q4_K_M · 4 GB

5.45 tok/s median

MATCHED CPU CONTROL

Throughput, with the full context.

InfernoRTllama.cpp
Llama 3.2 1B4 GiB · ctx 877 · out 131+29.1%
InfernoRT
71.56
llama.cpp
55.45
Llama 3.2 3B8 GiB · ctx 877 · out 131−20.7%
InfernoRT
26.93
llama.cpp
33.96
Llama 3 8B8 GiB · ctx 877 · out 131+11.7%
InfernoRT
16.04
llama.cpp
14.36
Llama 3.1 8B8 GiB · ctx 877 · out 131−5.1%
InfernoRT
11.98
llama.cpp
12.62
Llama 3 8B · constrained4 GiB · ctx 1,733 · out 257Runs at 4 GiB
InfernoRT
5.45
llama.cpp
OOMNo valid result under the 4 GiB cap
Llama 3.1 70B20 GiB · ctx 113 · out 13Runs at 20 GiB
InfernoRT
0.0129
llama.cpp
OOMExceeded the 20 GiB cap
Decode speed in tokens/second; higher is faster. Bar labels take a minimum width for readability. The 8B / 4 GiB and 70B / 20 GiB rows demonstrate memory feasibility, not a speed win. Results vary by workload and device.

CONSTRAINED CAPABILITY

For products that
have to run here.

From industrial equipment to private enterprise systems, local inference keeps execution close to the data. Validate memory fit, quality, and speed on your target hardware before production.

ROBOTICS & AUTONOMY INDUSTRIAL EDGE DEFENCE & SOVEREIGN PRIVATE ENTERPRISE AI

INTERACTIVE PLANNER

What could your hardware do?

First check whether the model fits your current hardware. Then estimate the fleet value of avoiding a more expensive device configuration.

Hardware fit · fleet value
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.

COMMERCIAL MODEL

Start with proof. Scale by device.

InfernoRT is sold around deployment value: hardware avoided, cloud cost displaced, privacy retained.

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 benchmark pack.

  • Runtime integration
  • Quality + memory comparison
  • Deployment recommendation

PRODUCTION & ENTERPRISE

Production deployment

From $50/device

Volume pricing for edge products, plus Enterprise & Sovereign private options.

  • Production runtime licence
  • Device-class validation
  • Private distribution & priority 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.

What hardware is supported today?

The current development and validation focus is macOS/Apple Silicon CPU, with Linux support in progress.

One device. One model.
A measurable next step.

Start with a technical fit assessment for your deployment.