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Humanoid robots have no universal DRAM requirement. The amount depends on the models, sensor pipelines, and software that must run onboard at the same time, along with latency, power, and thermal limits. Published NVIDIA platform examples range from 8GB to 128GB, but those are examples of hardware configurations—not a minimum or average for humanoid robots.
Why there is no single DRAM requirement
DRAM is the working memory a computer uses while software and data are active. In a humanoid robot, that memory may be shared by operating-system services, robotics middleware, camera and other sensor data, perception and localization software, planning, control, and locally running AI models. The robot needs enough usable memory for the combination of tasks that operate concurrently—not merely for one model considered by itself.
Consequently, two robots with similar bodies can have different memory needs. One might send high-level reasoning to a server and run a compact perception model locally; another might process several camera streams and run larger language or vision-language models onboard. The robot’s required capacity follows from its design and workload, not from its humanoid shape.
Official NVIDIA materials illustrate several possible platform capacities: 8GB, 64GB, and 128GB. They do not establish a cross-industry minimum, average, or representative specification for deployed humanoid robots. Treat the numbers as examples of particular edge-computing configurations, not as requirements for a robot category.
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What drives onboard memory use?
Models loaded at the same time
Language models, vision-language models, perception models, and robot policies all need working memory when loaded and executing. A system may run more than one model, or keep models resident while sensor processing and other software continue. That concurrent workload can place greater demands on DRAM than the largest model’s advertised parameter count suggests.
Model selection therefore matters in two ways: the memory footprint of each model and whether it must share the robot with other models and processes. A system designed to invoke a model occasionally may have different peak demand from one that keeps multiple capabilities available together. The relevant figure is the integrated workload’s measured peak, not a model-size label by itself.
Precision, activations, and runtime overhead
Quantization can reduce the memory required for model weights, and NVIDIA’s edge-AI materials include quantized-model examples. But weights are only one part of runtime memory. Execution also uses memory for activations, caches, the inference framework, the operating system, and supporting processes. A model’s parameter count—or the storage space occupied by a model file—does not directly equal the DRAM capacity needed to run it reliably.
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Sensors and perception pipelines
Camera streams and other sensor inputs require buffers and processing as they move through perception and localization. The number and rate of inputs, and how the software pipeline handles them, affect memory pressure. NVIDIA identifies real-time video, sensor processing, perception, planning, and localization as relevant robotics workloads.
The cited platform materials do not give a general number of gigabytes per camera or a formula that converts a humanoid’s sensor count into DRAM capacity. A camera count alone is therefore not a sound sizing rule: the meaningful test is the intended sensor configuration running through the actual pipeline.
Concurrency and response-time requirements
Robotics workloads do not necessarily take turns. A robot may need to process sensor input, localize itself, plan a movement, and run control software while also handling a higher-level AI task. If several components must remain active together, they compete for memory at the same time.
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Latency expectations influence that concurrency. NVIDIA distinguishes latency-critical real-time control from higher-level reasoning that can tolerate longer processing. A design that keeps time-sensitive work local has different requirements from one that can defer some reasoning or send it to a server. The choice changes what must be available on the robot, although the cited material does not prescribe a universal memory amount for either arrangement.
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Capacity is only one hardware constraint
Memory capacity should be considered alongside bandwidth, compute capability, power, and thermal limits. A system needs to move data and execute its workload within the robot’s available energy and cooling envelope; a larger DRAM figure alone does not show that a module is suitable. Compare exact configurations and the workloads they support rather than ranking platforms on capacity alone.
Published platform examples: what the figures do and do not show
The following figures come from NVIDIA materials and describe selected platforms or developer-kit configurations. They demonstrate a range of examples, not a market survey or a neutral ranking of humanoid-robot hardware.
| Example | Published memory or related figure | How to interpret it |
|---|---|---|
| Jetson Orin Nano Super | 8GB; NVIDIA’s 2025 edge-AI guide uses it as an example for smaller language models. | An example of a smaller-model edge system, not a minimum for humanoid robots. |
| Jetson AGX Orin | 64GB; NVIDIA’s 2025 guide uses it for larger-model examples, including language and vision-language use cases. | A platform example for more demanding local model workloads, not a universal target. |
| Jetson AGX Thor developer-kit configuration | 128GB; NVIDIA describes it for edge physical-AI workloads. The cited article’s publication date is not established here. | A developer-kit configuration example; the figure does not establish what every deployed robot needs. |
| Jetson Thor | 273GB/s listed memory bandwidth on NVIDIA’s product page; a publication date for that page is not established here. | Bandwidth is distinct from capacity and should be compared with the exact configuration and workload. |
| Jetson T4000 | 64GB LPDDR5x in a 40–70W envelope, according to NVIDIA’s 2026 platform example. | Shows why memory type and power envelope belong in a platform comparison as well as capacity. |
These figures come from different product materials and do not make all platforms directly interchangeable. Check the exact module or developer-kit configuration, memory specification, power envelope, and supported software before drawing a comparison. Product configurations and availability can change.
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“Robot memory” can refer to several different machines in a development pipeline. NVIDIA describes a humanoid workflow that separates 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. These machines serve different roles; memory used to train a model or host simulation is not automatically part of the robot’s onboard DRAM requirement.
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When estimating capacity, define the boundary first. If the question is how much DRAM the robot carries, count the onboard computer and the software it must execute there. Do not include training-cluster or simulation-host memory unless the question is specifically about those systems. Exact system boundaries vary by architecture, so state what is local, what runs elsewhere, and how the robot communicates with remote compute.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical process for estimating DRAM
No universal sizing formula or fixed headroom percentage is established by the cited platform materials. Use measurements from the intended integrated workload rather than adding a guessed allowance to a model’s parameter count.
- Define what must run onboard. List perception, localization, planning, policy inference, speech, safety and control software, and any other local functions. Note which tasks must operate concurrently and which can be deferred or run remotely.
- Select candidate models and precision. Identify the actual language, vision-language, perception, or policy models to deploy, including their intended quantization or other precision choices. Do not use model-file size as a substitute for runtime memory measurement.
- Build the real sensor pipeline. Use the intended sensor set and rates, and include the buffers and processing stages used by perception and localization. The cited sources supply no general per-camera memory allowance, so measure the chosen implementation.
- Measure peak use under the target runtime. Run the selected models and software together on the candidate platform while the sensors operate at their planned rates. Observe memory use during the workload, including periods when concurrent tasks are active.
- Account for the rest of the system. Include operating-system services, middleware, inference frameworks, caches, and other processes that share DRAM. Check reserved memory and software or pipeline choices that may affect what is usable.
- Set headroom from the measured workload. Allow room for peaks and expected updates, but choose that margin based on the deployment’s measurements and operating needs. The available NVIDIA materials do not prescribe a generally valid percentage.
- Compare complete platform constraints. Evaluate capacity with bandwidth, compute, power, thermal design, and I/O. Confirm the exact memory configuration of the candidate module, since product variants can differ.
Repeat the measurement after meaningful changes to models, precision, sensor rates, software versions, or concurrency. Such changes can alter the workload; a capacity that appeared adequate for an earlier configuration should not be assumed to fit a revised one without checking.
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- Calling one example a minimum. NVIDIA’s 8GB, 64GB, and 128GB examples refer to different platforms and workloads; none establishes a minimum for all humanoids.
- Counting only model weights. Runtime memory also serves activations, caches, frameworks, the operating system, sensor buffers, and concurrent processes.
- Assuming every task is local—or assuming it is remote. The split between onboard and server-side work is an architectural choice. Size the hardware for the functions the robot actually has to perform locally.
- Confusing bandwidth with capacity. A bandwidth figure such as 273GB/s describes a different property from a capacity figure such as 128GB. Both may matter, but one does not replace the other.
- Ignoring the full platform envelope. A capacity figure alone does not capture power, thermal, compute, or I/O constraints. Check those for the particular configuration and workload.
- Applying an optimization result to another board without testing. Software-stack or pipeline changes may reclaim memory in particular configurations, but their effect is deployment-specific.
What can be concluded from the available platform examples?
The sound conclusion is not that humanoids need a particular number of gigabytes. It is that onboard DRAM demand depends on the work the robot performs locally and simultaneously. NVIDIA’s published examples show that edge-AI configurations can span 8GB through 128GB, while its T4000 example also illustrates a 64GB LPDDR5x system with a stated 40–70W envelope. Those examples help frame choices, but they neither define the complete hardware market nor establish a typical deployed humanoid specification.
For a specific robot, the useful answer comes from a workload inventory and a peak-memory measurement on the intended runtime platform. Choose capacity only after including models, sensors, software overhead, concurrency, and practical headroom, then verify that bandwidth, compute, power, thermal behavior, and I/O also fit the design.
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