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The direct answer: size for peak concurrency
A model that loads successfully by itself can still fail on a robot. Perception, localization, mapping, planning, control, logging, communications, and an operating system may all allocate memory at the same time. A language or vision-language model can add a large, variable context and workspace requirement.
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Use this engineering rule:
- Capacity: enough memory for every process that must remain resident during the robot’s busiest operating state, plus measured headroom.
- Performance: enough bandwidth and compute throughput to move weights and sensor data while meeting the robot’s latency target.
- Sustainability: performance that remains stable inside the module’s power and thermal envelope.
- Fit: a module, carrier board, driver stack, and sensor interfaces that support the intended software and physical design.
The final choice is valid only after a concurrent test of the real software stack. Published model-to-device examples are useful starting points, not guarantees that your robot will fit or respond fast enough.
What consumes memory in a robot?
Start an inventory before comparing modules. Record the resident and peak allocation for each category, the process that owns it, and whether it can be released between tasks.
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Model weights
Weights are the stored parameters for perception networks, detectors, segmentation models, depth estimation, speech systems, language models, or vision-language models. Record the format actually loaded by the runtime, including quantization. A compressed model file, an expanded runtime representation, and temporary conversion buffers can have different footprints.
Runtime, activations, and workspace
Inference frameworks allocate activation tensors, scratch space, operator workspaces, graph caches, and memory for preprocessing and postprocessing. These allocations often rise with image resolution, batch size, sequence length, or the number of simultaneous streams. Measure the runtime allocation rather than treating the model-file size as the requirement.
Context and history
Language and vision-language systems retain tokens, image features, conversation history, or task state. Longer context increases memory use even when the model weights do not change. A robot that only needs short commands may have a very different requirement from one that keeps a long scene or dialogue history.
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Camera frames, depth images, point clouds, inertial data, radar, and compressed or decoded video consume memory. Multiple cameras and higher frame rates multiply buffer pressure. Account for capture queues, color conversion, synchronization copies, and any buffers shared with accelerators.
Mapping, localization, and planning
Occupancy grids, point-cloud maps, visual features, pose graphs, cost maps, navigation graphs, and planner search data can remain resident for the entire mission. Their peak size may depend on the mapped area, resolution, route complexity, or recovery behavior rather than on the AI model.
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Operating system and middleware
Include the kernel, device drivers, robotics middleware, message queues, visualization, telemetry, logging, networking, security services, and your application code. Reserve memory for startup spikes, diagnostics, and safe shutdown; a steady-state reading alone can hide a launch-time failure.
Headroom and growth
Leave measured headroom for allocator fragmentation, software updates, additional sensors, larger maps, longer missions, and model revisions. Do not choose a tier that passes only when every queue and cache is at its minimum.
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Capacity is not the same as bandwidth
Capacity answers whether the workload fits. Bandwidth affects how quickly weights and sensor data can move through the pipeline. A module can have enough gigabytes but still miss a control or perception deadline if memory traffic is the bottleneck. Conversely, very high peak bandwidth does not prove application performance; compare it with measured end-to-end latency on the actual robot stack.
| Decision axis | What to measure | Why it changes the choice |
|---|---|---|
| Capacity | Peak resident and temporary allocations for all concurrent processes | Prevents out-of-memory failures and forced model unloading |
| Bandwidth | Observed memory throughput and application latency under sensor and inference load | Determines whether data movement meets frame-rate and response-time targets |
| Power | Configured and sustained module power during the mission | A larger or faster tier may exceed the robot’s battery or regulator budget |
| Thermals | Temperature and performance over a long, enclosed run | Cooling limits can reduce sustained clocks and erase a benchmark advantage |
| Software fit | Driver, framework, model, accelerator, and sensor-interface compatibility | A theoretically suitable module is unusable if the stack does not support it |
| Lifecycle | Availability, exact configuration, carrier-board fit, and replacement plan | Integrated memory makes the initial module choice a long-term platform decision |
For reference, NVIDIA lists Jetson Thor at 273 GB/s memory bandwidth and a 40–130 W series power range. Those are platform specifications, not a latency result for a particular robot.
How to size memory step by step
- Define the mission and deadlines. Write down camera count and resolution, depth or point-cloud rates, control-loop frequency, acceptable perception-to-action latency, mission duration, and whether an on-device language or vision-language model is mandatory.
- List every always-on process. Include operating-system services, middleware nodes, drivers, logging, networking, safety monitors, localization, mapping, planning, control, and user interfaces. Mark which processes overlap in time.
- Record each model’s real runtime footprint. Measure weights after loading, activation and workspace allocations, preprocessing, postprocessing, and any conversion or compilation cache. Record the model version, quantization, input shape, batch size, and runtime version with the measurement.
- Measure sensor buffering. Count buffers per stream, queue depth, pixel or point-cloud format, and copies between CPU, GPU, and accelerators. Repeat with the maximum camera and depth rates the robot can produce.
- Account for context and mission state. Set the intended token or image-history limit, map size, route complexity, and recovery behavior. Test the longest context and largest map that the product specification allows, not only a short demonstration.
- Run the concurrent stack. Launch perception, localization, mapping, planning, control, logging, and language features together. Include startup, model reload, sensor reconnect, and fault-recovery paths, because temporary allocations can exceed steady state.
- Capture peaks and latency. Log total memory, per-process allocations, allocator failures, frame drops, queue growth, and end-to-end timing. Test the worst credible combination: maximum sensor load, largest map, longest context, and simultaneous inference.
- Stress power and cooling. Run long enough for the enclosure and heatsink to reach steady temperature. Check whether clocks, frame rate, or control latency degrade. A short benchmark can pass while a sustained mission fails.
- Select the next memory tier above the measured peak. Add explicit engineering headroom for software growth and fragmentation. If the next tier changes power, carrier-board, or mechanical requirements, repeat the system-level comparison rather than selecting on gigabytes alone.
- Freeze a reproducible acceptance test. Keep the workload, software versions, sensor recordings, context limit, and pass/fail thresholds. Re-run it after model, driver, firmware, or map changes.
NVIDIA Jetson memory tiers as reference points
The following figures are current NVIDIA specifications for the named module families. Confirm the exact module and developer-kit configuration before procurement because product names and available combinations can differ.
| Module family | Listed memory configurations | Listed power range | How to interpret it |
|---|---|---|---|
| Jetson Orin Nano | 4 GB or 8 GB | 7–15 W | Lower-power edge designs; validate whether all robot services fit alongside the model |
| Jetson Orin NX | 8 GB or 16 GB | 10–25 W | More capacity and power headroom than Orin Nano, subject to the same workload test |
| Jetson AGX Orin | 32 GB or 64 GB | 15–60 W | Suitable for larger or more concurrent workloads when the robot can supply and cool the configured power |
| Jetson Thor series | 128 GB 256-bit LPDDR5X | 40–130 W | High-capacity platform for substantially larger models and data movement; system integration is correspondingly demanding |
NVIDIA lists 273 GB/s for the 128 GB Jetson Thor memory configuration. The AGX Thor Developer Kit specifications also list 1 TB of NVMe storage. NVMe stores models, maps, logs, and datasets; it is not system RAM and does not add RAM bandwidth.
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Integrated memory changes the purchase decision
Memory is integrated on the Jetson modules discussed here. You cannot plan on adding desktop-style DIMMs later. Treat capacity as part of the module selection, and verify the exact memory tier, carrier-board compatibility, thermal solution, power configuration, and regional availability before ordering. An external SSD can expand persistent storage, but it cannot rescue a workload that runs out of system memory.
Can a Jetson Orin Nano run my robot’s AI model?
Only a concurrent test can answer for your robot. NVIDIA’s published examples illustrate possible model tiers, but they are vendor guidance rather than independent benchmark guarantees.
| Vendor-published example | What it demonstrates | Important qualification |
|---|---|---|
| 8 GB Orin Nano Super with Llama 3.2 3B or Phi-3 | Compact language-model deployments may fit on a low-memory module | Context, quantization, runtime workspace, and robotics processes determine actual fit and latency |
| 64 GB AGX Orin with gpt-oss-20b or quantized Llama 3.1 70B | A larger memory tier can host substantially larger language-model examples | These are illustrations, not a promise of a specified token rate or robot response time |
| 128 GB Thor with 100B-plus-parameter models | Thor is positioned for very large model examples | Model format, context, software version, power, and thermal behavior still require validation |
| 8 GB Orin Nano with Qwen 3.5 2B and a 16,384-token context | An additional NVIDIA example of a small model and stated context | Verify the cited model and runtime versions and test alongside the complete sensor stack |
| Thor with the Gemma 4 31B family or a 128K Gemma 3 context | Illustrates high-capacity language workloads | Vendor-authored guidance; a long context can materially change memory use and latency |
The examples above come from NVIDIA material published December 11, 2025, and a NVIDIA Perspectives article last updated May 11, 2026. The Perspectives pages caution that AI-generated content can be incomplete or erroneous, so cross-check model and platform details against current product documentation and your own acceptance test.
Choosing between tiers without a misleading benchmark
Compact perception and control
If the robot uses compact perception models, modest maps, a small number of cameras, and no large local language model, begin by measuring an Orin Nano configuration that meets the control deadline. The 4 GB and 8 GB options are not interchangeable: leave enough room for the operating system, middleware, buffers, and growth rather than allocating nearly all memory to weights.
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Several sensors or concurrent AI services
Multiple high-resolution streams, depth processing, mapping, and a language assistant can push a design toward an Orin NX or AGX Orin tier. Compare the 8 GB, 16 GB, 32 GB, and 64 GB choices against measured peak allocation and sustained power. More capacity is useful only if the carrier board, cooling, battery, and software stack can support it.
Large models and long context
Large language or vision-language models, long histories, and substantial maps require a high-capacity platform such as a 64 GB AGX Orin or a 128 GB Thor configuration in the vendor examples. Treat those examples as starting points; a model that fits in memory can still fail the robot’s latency or thermal requirement.
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Memory selection is a whole-robot decision. NVIDIA lists Orin Nano at 7–15 W, Orin NX at 10–25 W, and AGX Orin at 15–60 W. Thor’s listed series range is 40–130 W. The configured value, not only the family maximum, affects battery sizing, voltage regulation, enclosure airflow, fan noise, and mission duration.
- Check the carrier board’s input and regulation limits at the selected power mode.
- Verify that the heatsink, fan, or passive enclosure can remove sustained heat, not just a short burst.
- Measure battery energy consumed during the actual mission, including sensors and motors; the module’s rated power is only one part of the robot budget.
- Confirm connector count, camera interfaces, storage, mechanical envelope, and environmental requirements before treating a module as deployable.
A faster memory subsystem may reduce processing time, but if it forces aggressive cooling or shortens mission endurance, the system-level result may be worse. Compare sustained latency and energy per mission, not a peak bandwidth number in isolation.
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Because the memory is integrated, capacity mistakes can require replacing the compute module rather than installing an inexpensive upgrade. Before committing funds, document:
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- Exact module, memory tier, power mode, and developer-kit or production configuration.
- Carrier-board, camera, depth, networking, and storage compatibility.
- Current driver, firmware, operating-system, and inference-runtime support for every model.
- Measured peak memory, latency, thermal behavior, and power on a representative robot.
- Expected model, sensor, map, and software growth over the product’s service life.
- Supply availability, replacement strategy, and whether the selected configuration is offered in the deployment region.
No neutral cross-vendor cost/performance benchmark is established for a specified robot workload here. Compare prices, availability, support, and integration labor using the same acceptance test rather than ranking vendors from memory capacity alone.
Common sizing mistakes and fixes
Buying for the model file size
Symptom: the model loads in a desktop test but the robot reports allocation failures or drops frames. Fix: measure weights, activations, workspace, preprocessing, context, and concurrent services on the target module.
Testing one process at a time
Symptom: each node passes independently, but simultaneous operation causes latency spikes. Fix: replay peak sensor traffic while all resident nodes and logging are active.
Ignoring context and map growth
Symptom: a short demo works, then long missions or large environments exhaust memory. Fix: test the maximum supported context, map, queue depth, and recovery path; set explicit limits if necessary.
Confusing storage with RAM
Symptom: adding an SSD does not stop out-of-memory errors. Fix: use SSD capacity for persistent files and logs, while selecting sufficient integrated system memory for active allocations.
Using peak bandwidth as a performance promise
Symptom: a specification looks fast, but end-to-end response time misses the requirement. Fix: measure sensor-to-action latency, frame drops, and sustained throughput under the complete workload.
Skipping thermal soak tests
Symptom: a five-minute benchmark passes but a long mission slows down. Fix: run until the enclosure reaches steady temperature and record clocks, latency, and power over time.
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A practical decision rule
Select the smallest integrated-memory tier that passes the complete peak-concurrency test with documented headroom, required latency, and sustained thermal performance. Move up a tier when the measured workload leaves too little room for model, sensor, map, or software growth; move to a different platform when bandwidth, power, cooling, interfaces, or lifecycle constraints—not capacity—are the limiting factor. This method is more reliable than choosing by parameter count, model-file size, or a vendor example alone.
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