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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11There is no universal DRAM requirement for a humanoid robot. The right capacity depends on what the robot must process onboard at the same time: sensor data, perception, localization, planning, control software and any locally running AI models. Published NVIDIA platform examples range from 8GB to 128GB, but those are examples of hardware configurations—not a minimum, average or requirement for every humanoid.
Why humanoid robots do not have one DRAM requirement
DRAM is working memory: it holds the data and software a computer needs while it operates. For a humanoid, the relevant question is not simply how large one model is, or how much memory a robot in general ought to have. It is how much memory the complete onboard system needs when its required jobs run together.
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A robot using compact local models and sending some higher-level reasoning elsewhere can have different onboard needs from one expected to process several camera streams and run large multimodal models locally. The difference comes from the workload and where it runs, not from the word “humanoid” alone. No cross-industry minimum or average capacity is established by the cited platform material.
Memory capacity is also not a stand-in for overall capability. Two computers with the same DRAM capacity may differ in memory bandwidth, compute resources, power envelope and supported software. A capacity figure is useful only when considered alongside the work the system is meant to perform.
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First decide which computer you are sizing
Humanoid-robot projects may use several kinds of computing systems, and their memory should not be conflated. NVIDIA describes a workflow with an AI supercomputer for model training, an OVX system for simulation and synthetic data, and an onboard runtime computer, such as Jetson Thor, for low-latency inference. Those roles represent different stages and locations in a workflow; the memory in a training or simulation host is not the robot’s onboard DRAM requirement.
The boundary is architecture-dependent. A team may distribute tasks differently, so start by identifying which functions must execute on the robot and which can run on another system. If a specification says a project uses a certain amount of memory, check whether it describes the robot itself, a developer kit, a simulation host or a training system before treating it as an onboard requirement.
What determines onboard DRAM capacity
Models loaded and active at the same time
Language, vision-language, perception and robot-policy models can all consume working memory while loaded and executing. The total pressure rises when a system must keep multiple models or processes active concurrently. A robot that runs a model only when needed may have a different peak from one that keeps several functions ready at once.
Model size matters, but parameter count alone does not tell you the final DRAM requirement. The amount used during execution can also depend on how the model is deployed, what intermediate data it produces and what else the system is doing at that moment.
Precision and runtime overhead
Quantization can reduce model-memory needs; NVIDIA’s edge-AI guide includes quantized model examples. But the model’s nominal size is only one part of the working set. Activations, caches, frameworks, operating-system services and other running processes also use memory. Sizing from a model’s parameter count, without measuring the deployed runtime, can therefore understate the capacity required.
Software choices can affect the total, too. NVIDIA describes memory reclamation through changes to the software stack and processing pipeline. Its examples are specific to the configurations discussed there; they do not establish a universal saving that can be assumed for another board or robot.
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Sensors and perception pipelines
Camera and other sensor streams create input data that the robot’s software must handle for tasks such as perception and localization. The number of streams, their rates and the processing pipeline all affect the system’s workload. NVIDIA identifies sensor processing, real-time video, perception, planning and localization as relevant robotics workloads.
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The cited material does not give a general number of gigabytes per camera or a formula for estimating memory from a humanoid’s sensor count. A camera count by itself is not enough to calculate capacity: the robot’s actual data flow and software need to be measured.
Concurrency and response-time goals
Robots may need perception, planning and control to proceed together. If several functions must be active at once, the peak working set can be larger than when they run separately. Response-time requirements influence that design: NVIDIA distinguishes latency-critical real-time control from higher-level reasoning that can tolerate longer processing time.
This distinction does not prescribe a particular memory amount. It helps define which functions need to be available locally and concurrently, which is information a capacity estimate needs.
Power, bandwidth and the physical platform
DRAM capacity is only one platform constraint. Bandwidth describes how quickly data can move through memory; a system can have ample capacity yet still need to be evaluated for the data movement its workload requires. Compute resources, power and thermal limits also matter when choosing a module. For example, NVIDIA’s Jetson Thor developer-kit configuration pairs 128GB with a 130W envelope, while its Jetson T4000 example lists 64GB of LPDDR5x in a 40–70W envelope.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThose examples are not a like-for-like performance ranking. The products and configurations differ, and the cited figures do not establish what a particular robot will consume or achieve. Verify the exact module configuration and evaluate capacity, bandwidth, compute, power, thermal design and I/O together.
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Published platform examples are reference points, not requirements
The figures below illustrate selected NVIDIA platform examples. They show why capacities vary with intended workloads, but they do not define a humanoid-robot standard or represent the entire market.
| Platform example | Published memory figure | Context stated by NVIDIA | Other stated specifications |
|---|---|---|---|
| Jetson Orin Nano Super | 8GB | NVIDIA’s 2025 edge-AI guide uses it as an example for smaller language models. | Bandwidth and power figures for this example are not stated in the cited NVIDIA guide. |
| Jetson AGX Orin | 64GB | NVIDIA’s 2025 edge-AI guide uses it as an example for larger models, including language and vision-language use cases. | Bandwidth and power figures for this example are not stated in the cited NVIDIA guide. |
| Jetson AGX Thor developer kit | 128GB | NVIDIA’s Thor article presents this as a configuration for demanding edge physical-AI workloads; the page result did not expose a publication date. | The cited NVIDIA material pairs it with a 130W envelope and lists 273GB/s memory bandwidth; the page result did not expose a publication date for the bandwidth figure. |
| Jetson T4000 | 64GB LPDDR5x | The cited NVIDIA material presents a platform example; it does not state a specific humanoid workload for this figure. | 40–70W envelope, stated by NVIDIA in 2026; bandwidth is not stated in the cited material. |
These are vendor examples, not a neutral survey of deployed humanoids. The figures do not establish that all robots need at least 8GB, that 128GB is the upper end of the market, or that one listed platform is the right choice for a particular design. Product variants can differ, so confirm the exact memory configuration for the system being considered.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to estimate memory for a real robot
A useful estimate comes from the integrated workload rather than a single model specification. The following process turns the robot’s intended jobs into a measured peak and gives the design team a basis for comparing candidate modules.
- Define the boundary. List what must run onboard and separate those jobs from model training, simulation and other off-robot work.
- List simultaneous functions. Include the relevant perception, localization, planning, policy inference, speech and safety or control software. Mark which functions must be active together rather than assuming every job runs independently.
- Select candidate models and precision. Record the model versions and deployment precision being considered. Include every locally deployed model, not just the largest one.
- Measure model execution in the intended runtime. Measure peak memory use with the actual software stack. Do not substitute parameter count or the model file size for the runtime measurement.
- Add the rest of the workload. Account for sensor buffers, the operating system, middleware, inference runtime and other processes that operate alongside the models.
- Run the integrated system at its intended operating conditions. Exercise the workload at the desired sensor rates and response-time targets, then observe peak memory while all required functions are running.
- Choose and verify the platform. Compare candidate modules on capacity, bandwidth, compute, power, thermal design and I/O. Recheck the exact memory configuration of the selected variant.
- Plan for measured peaks and change. Leave room for observed peaks and updates, using the system’s actual workload measurements to guide the decision. The cited vendor material does not prescribe a universal headroom percentage.
This process is an engineering approach, not a formula supplied by a platform vendor. The available examples do not provide a universal sizing equation, per-camera allowance or fixed reserve that can be applied to every robot.
Common sizing mistakes to avoid
- Treating one platform’s capacity as an industry minimum. The published 8GB, 64GB and 128GB examples correspond to different systems and workload contexts; none establishes a universal threshold.
- Counting model parameters and stopping there. Runtime overhead, activations, caches, sensor data and concurrent software contribute to memory pressure beyond the model’s nominal size.
- Ignoring concurrency. A test that runs one component at a time may miss the peak that occurs when perception, planning and control overlap.
- Confusing onboard memory with development-system memory. Training and simulation roles are separate from the robot’s runtime computer in NVIDIA’s described workflow.
- Comparing capacity without the rest of the specification. Bandwidth, compute, power and thermal limits can matter alongside DRAM capacity, and the cited platform examples are not interchangeable benchmarks.
- Assuming an optimization result transfers automatically. Memory reclamation depends on software and pipeline choices; measure the result on the target configuration rather than applying another system’s reported savings.
- Assuming a particular sensor count yields a fixed memory amount. No general per-camera figure or humanoid-wide sensor-memory formula is established by the cited material.
What the published figures can—and cannot—tell you
The examples show a range of memory configurations used in NVIDIA materials for edge AI and physical-AI contexts. They help readers understand that smaller-model examples and more demanding edge workloads may be associated with different platform capacities. They do not tell you the average memory in deployed humanoids, establish a minimum specification, or prove that a particular robot can run a particular workload at a required latency.
For an engineering or purchasing decision, the useful figure is the measured peak for the intended onboard workload on the candidate system, with the relevant configuration and operating conditions recorded. For a general reader, the practical takeaway is simpler: ask what runs on the robot, what must run together, and whether memory, bandwidth, compute and power match that workload.
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