Adaptive Runtime

Coming Q2 2026 — This is a design preview. Request early access to be notified when available.

Overview

The Adaptive Runtime dynamically adjusts inference execution to stay within energy and thermal constraints. Instead of failing when resources are limited, it gracefully degrades while maintaining output quality bounds.

Key Capabilities

Dynamic precision — Switch between FP16/INT8/INT4 based on power
Layer skipping — Skip non-critical layers when energy-constrained
Context adaptation — Reduce context window under pressure
Thermal throttling — Automatic frequency scaling near thermal limits
Quality guarantees — Bounded degradation with quality metrics

How It Works

Input Request

Your inference request arrives with energy/thermal constraints specified.

Monitor State

Energy Monitor tracks battery level, solar input, and power draw. Thermal Monitor tracks CPU/GPU temperatures and cooling capacity.

Adaptation Controller

Based on current constraints and monitor data, the controller makes decisions:

Precision selection (FP16/INT8/INT4)
Layer skip decisions
Context window sizing
Batch size adjustment

Inference Engine

Executes the model with the selected adaptations applied.

Output + Adaptation Report

Returns the response along with a detailed report of what adaptations were applied.

API Preview

Submit with Energy Constraints

from rotastellar import RotaStellarClient

client = RotaStellarClient(api_key="rs_...")

result = client.runtime.generate(
    model="llama-70b",
    prompt="Summarize this document...",
    constraints={
        "energy_budget_wh": 0.5,      # Max energy for this request
        "thermal_limit_c": 75,         # Throttle above this temp
        "quality": "best_effort"       # or "exact"
    }
)

print(f"Response: {result.text}")
print(f"Energy used: {result.energy_wh} Wh")
print(f"Adaptations applied: {result.adaptations}")

import { RotaStellarClient } from '@rotastellar/sdk';

const client = new RotaStellarClient({ apiKey: 'rs_...' });

const result = await client.runtime.generate({
  model: 'llama-70b',
  prompt: 'Summarize this document...',
  constraints: {
    energyBudgetWh: 0.5,
    thermalLimitC: 75,
    quality: 'best_effort'
  }
});

console.log(`Response: ${result.text}`);
console.log(`Adaptations: ${JSON.stringify(result.adaptations)}`);

curl -X POST https://api.rotastellar.com/v1/runtime/generate \
  -H "Authorization: Bearer rs_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-70b",
    "prompt": "Summarize this document...",
    "constraints": {
      "energy_budget_wh": 0.5,
      "thermal_limit_c": 75,
      "quality": "best_effort"
    }
  }'

Adaptation Report

Every response includes what adaptations were applied:

{
  "text": "The document discusses...",
  "energy_wh": 0.42,
  "latency_ms": 156,
  "adaptations": {
    "precision": "int8",           // Reduced from FP16
    "layers_skipped": 4,           // Out of 80 total
    "context_used": 4096,          // Reduced from 8192
    "batch_size": 1                // No batching
  },
  "quality_metrics": {
    "estimated_perplexity_delta": 0.02,
    "confidence": 0.94
  }
}

Configure Adaptation Policies

Set global adaptation preferences:

client.runtime.configure(
    adaptive={
        # Precision bounds
        "precision_floor": "int8",        # Never go below INT8
        "precision_ceiling": "fp16",      # Start at FP16

        # Layer skipping
        "layer_skip_max": 0.2,            # Skip up to 20% of layers
        "skip_strategy": "importance",    # or "uniform", "early", "late"

        # Context management
        "context_min": 2048,              # Minimum context window
        "context_strategy": "truncate",   # or "summarize", "slide"

        # Thermal management
        "thermal_threshold_c": 70,        # Start throttling
        "thermal_critical_c": 85,         # Hard limit

        # Quality guarantees
        "quality_floor": 0.9              # Minimum acceptable quality score
    }
)

Adaptation Strategies

Precision Scaling

Precision	Relative Energy	Relative Quality
FP16	1.0x	1.0
INT8	0.5x	0.98
INT4	0.3x	0.92

# Force specific precision
result = client.runtime.generate(
    model="llama-70b",
    prompt="...",
    constraints={
        "precision": "int8"  # Fixed precision
    }
)

Layer Skipping

Skip less important layers to save energy:

# Allow aggressive layer skipping
result = client.runtime.generate(
    model="llama-70b",
    prompt="...",
    constraints={
        "layer_skip_max": 0.3,          # Up to 30%
        "skip_strategy": "importance"   # Skip least important
    }
)

print(f"Layers skipped: {result.adaptations['layers_skipped']}")

Context Adaptation

Reduce context window under constraints:

result = client.runtime.generate(
    model="llama-70b",
    prompt="...",
    context=long_document,  # 32k tokens
    constraints={
        "context_max": 8192,           # Limit context
        "context_strategy": "summarize" # Summarize overflow
    }
)

Quality Modes

Best Effort

Maximize quality within constraints, may degrade:

result = client.runtime.generate(
    model="llama-70b",
    prompt="...",
    constraints={
        "energy_budget_wh": 0.3,
        "quality": "best_effort"
    }
)
# Will adapt to fit energy budget

Exact

Fail if constraints can’t be met at full quality:

result = client.runtime.generate(
    model="llama-70b",
    prompt="...",
    constraints={
        "energy_budget_wh": 0.3,
        "quality": "exact"
    }
)
# Will fail if 0.3 Wh isn't enough for full precision

Bounded

Degrade only within specified bounds:

result = client.runtime.generate(
    model="llama-70b",
    prompt="...",
    constraints={
        "energy_budget_wh": 0.3,
        "quality": "bounded",
        "quality_floor": 0.95  # Must maintain 95% quality
    }
)
# Will adapt but not below 95% quality

Monitoring

Track adaptation patterns over time:

# Get adaptation statistics
stats = client.runtime.adaptation_stats(
    period="24h"
)

print(f"Total requests: {stats.total_requests}")
print(f"Adapted requests: {stats.adapted_requests}")
print(f"Average energy savings: {stats.avg_energy_savings_percent}%")
print(f"Average quality maintained: {stats.avg_quality_maintained}")

Getting Started

Planning Tools

Orbital Intelligence

CAE

Operator Agent

Mission Control

Orbital Runtime

Orbital Sim

Distributed Compute

SDKs

Resources

Adaptive Runtime

Adaptive Runtime

Overview

Key Capabilities

How It Works

API Preview

Submit with Energy Constraints

Adaptation Report

Configure Adaptation Policies

Adaptation Strategies

Precision Scaling

Layer Skipping

Context Adaptation

Quality Modes

Best Effort

Exact

Bounded

Monitoring

Next Steps

Resilient Compute

Orbit Scheduler

​Adaptive Runtime

​Overview

​Key Capabilities

​How It Works

​API Preview

​Submit with Energy Constraints

​Adaptation Report

​Configure Adaptation Policies

​Adaptation Strategies

​Precision Scaling

​Layer Skipping

​Context Adaptation

​Quality Modes

​Best Effort

​Exact

​Bounded

​Monitoring

​Next Steps

Resilient Compute

Orbit Scheduler

Adaptive Runtime

Overview

Key Capabilities

How It Works

API Preview

Submit with Energy Constraints

Adaptation Report

Configure Adaptation Policies

Adaptation Strategies

Precision Scaling

Layer Skipping

Context Adaptation

Quality Modes

Best Effort

Exact

Bounded

Monitoring

Next Steps