Orbital Runtime / Resilient Compute

When Bit Flips
Are Normal

In LEO, expect 10-100 single-event upsets per device per day. Standard ML frameworks have no concept of this. Resilient Compute detects corruption and recovers gracefully.

Get Early Access Research

Fault Injection Framework

Q2 2026

Research Benchmarks

Q3 2026

Production Runtime

2027

Fault Injection Simulator

Watch how Resilient Compute detects and recovers from radiation-induced bit flips in real-time.

Model Layer Visualization

Idle

Layer 0 (Input) 80 Transformer Layers Layer 79 (Output)

SEUs Injected

Detected

Recovered

Silent (Undetected)

Output Confidence 100%

Click "Inject Fault" or run inference to see fault detection in action

The Problem

Cosmic rays cause single-event upsets (SEUs) that flip bits in memory and logic. On Earth, these are rare. In LEO, they happen constantly.

Environment	SEU Rate (per device/day)	Framework Assumption
Sea level	~0.01	Ignored (ECC handles it)
Commercial aviation	~1	Ignored
LEO (400km)	10-100	Not supported
GEO / deep space	100-1000+	Not supported

Without Resilient Compute, that's 10-100 potential silent corruptions in your inference results every day. Memory ECC catches some, but not bit flips in registers, logic, or floating-point units.

Fault Tolerance Mechanisms

Activation Checksums

Lightweight checksums at layer boundaries detect corruption in intermediate activations. Less than 5% overhead, catches most SEU-induced errors.

Selective Re-execution

When corruption is detected, re-run only the affected layers - not the entire inference. Typical overhead: 3-8% vs 100% for full restart.

Redundant Critical Paths

Triple-execute attention layers and vote on results. Attention impacts output quality far more than feed-forward layers.

Uncertainty Quantification

Every output includes a confidence score. If fault recovery was needed, the score reflects potential quality impact.

Graceful Corruption Handling

Bound error propagation rather than fail completely. Flag uncertain outputs so downstream systems can handle appropriately.

Fault Injection Testing

Validate model behavior under simulated radiation before deployment. Know how your model degrades before it reaches orbit.

API Example

          Python - Resilient Inference
        

from rotastellar import ResilientCompute, FaultMode

compute = ResilientCompute(api_key="...")

result = compute.generate(
    model="llama-70b",
    prompt="Calculate orbital trajectory...",
    fault_tolerance=FaultMode.DETECT_AND_RECOVER,
    critical_layers=[0, 1, 2, -3, -2, -1],
    checksum_interval=4,  # Every 4 layers
    redundancy="attention_only"
)

print(f"Response: {result.text}")
print(f"Confidence: {result.confidence}")
print(f"Faults detected: {result.faults_detected}")
print(f"Faults recovered: {result.faults_recovered}")
        

          Fault Report
        

{
  "faults_detected": 1,
  "faults_recovered": 1,
  "fault_log": [
    {
      "layer": 23,
      "type": "checksum_mismatch",
      "action": "re_execute",
      "layers_rerun": [20, 21, 22, 23, 24, 25, 26, 27],
      "overhead_ms": 12,
      "result": "verified_correct"
    }
  ],
  "confidence": 0.97,
  "total_overhead_pct": 3.2,
  "redundant_executions": 6,
  "output_verified": true
}
        

Build radiation-tolerant AI

Get early access to the fault injection framework and resilience benchmarks.

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When Bit FlipsAre Normal