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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteTesla builds custom AI inference chips for its Full Self-Driving (Supervised) software and integrates them with its cameras and vehicle software. NVIDIA, Mobileye and Qualcomm offer platform families that automakers can use in their own vehicle programs. Those are different kinds of products, so a peak TOPS figure alone cannot show which car will drive better. And Tesla’s FSD (Supervised) remains a Level 2 driver-assistance system: the driver must stay attentive and responsible.
What is being compared: a chip, a platform or a driving system?
A driver-assistance computer processes sensor data and runs software used for functions such as detecting objects or supporting driving decisions. It is only one part of a vehicle’s ADAS (advanced driver-assistance system). Sensors, software, safety design, vehicle integration and the driver’s role also shape what the system can do.
Tesla describes an integrated automaker system: its custom FSD computer runs neural networks in its vehicles. NVIDIA, Mobileye and Qualcomm describe platform families offered to automakers, which then develop or integrate systems for their vehicles. A chip specification is therefore not a direct measure of a complete vehicle’s driving capability. Tesla’s 2024 Form 10-K and the companies’ platform descriptions illustrate this difference.
How the platforms compare on published information
| Company and platform | Positioning described by the company | Published performance figure in the cited material | What the figure does—and does not—establish |
|---|---|---|---|
| Tesla custom FSD computer | Custom inference chips integrated with Tesla’s FSD software and camera-based neural networks. | No full current production-chip specification table is stated on Tesla’s AI page. | Describes Tesla’s design approach, not a matched comparison with supplier platforms. Tesla AI & Robotics |
| NVIDIA DRIVE AGX Orin | Scalable automotive platform; NVIDIA also describes DRIVE Thor as a centralized computer that can combine ADAS and infotainment functions. | Up to 254 TOPS for the Orin SoC, as stated in NVIDIA technical documentation current in 2026. | A vendor-stated maximum for that SoC; it is not a standardized head-to-head result against Tesla or another platform. NVIDIA Autonomous Driving Safety Report |
| Mobileye EyeQ6 Lite and EyeQ6 High | EyeQ6 Lite is positioned for core Level 1–2 ADAS; EyeQ6 High for premium ADAS. | Mobileye says EyeQ6 Lite provides 4.5 times EyeQ4 Mid compute at similar power. It says EyeQ6H provides three times EyeQ5H compute at 25% more power. | Both are comparisons within Mobileye’s EyeQ line, not directly comparable cross-vendor measurements. Mobileye’s EyeQ6 description |
| Qualcomm Snapdragon Ride | An automated-driving platform for functions including navigation, obstacle detection and safety-focused decision-making. | A matched hardware model or comparable performance figure is not stated on the cited page. | The platform description establishes intended scope, but not a fair performance ranking against the other entries. Qualcomm Automated Driving |
What Tesla says its custom hardware does
Tesla’s AI page describes custom inference chips intended to run FSD software. It identifies neural-network tasks including camera-level semantic segmentation, object detection and depth estimation, as well as bird’s-eye-view networks that combine camera video to estimate road layout and 3D objects. Tesla emphasizes throughput, latency, correctness, determinism, memory efficiency and low-level software integration; the page does not supply a full current production-chip specification table. Tesla AI & Robotics
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Tesla’s evidence dashboard describes a system with eight high-resolution cameras and a custom-silicon computer. It calls FSD (Supervised) an SAE Level 2 system. Tesla’s 2024 Form 10-K likewise says its FSD computer runs the company’s neural networks in its vehicles. Those descriptions concern Tesla’s product and integration approach; they do not establish how its compute compares with another maker’s under equivalent conditions.
FSD (Supervised) is not autonomous driving
Tesla says FSD (Supervised) is designed and deployed as an SAE Level 2 system, with an attentive driver retaining responsibility for driving operation. The dashboard discusses the distinction between a system’s design intent for dividing roles and responsibilities and its technological capability; that distinction does not change the stated Level 2 driver responsibility. Tesla FSD (Supervised) Dashboard
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What Tesla’s AI5 and AI6 plans mean
In its Q4 2025 update, published January 28, 2026, Tesla said its custom AI5 and AI6 inference chips were in development, with production planned for 2027 and 2028, respectively. These dates are company plans, not confirmation that either chip is in production. Tesla Q4 2025 AI & Software update
Tesla also set a target of 50 times AI4 performance for AI5, describing the target in terms of 10 times raw compute, nine times memory capacity and five times hardened-block quantization and softmax. These are Tesla’s stated target and component comparisons, not an independently verified, standardized result against the other platforms in this comparison. The update does not make the target a direct measure of on-road capability.
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Why TOPS does not identify the best driver-assistance computer
TOPS means trillions of operations per second, but a number is meaningful only with its measurement context. Vendors may report different precision formats, chip configurations and workloads. Peak compute also does not by itself describe what a system can sustain within its memory, power and thermal limits, or how effectively its software uses the hardware.
The cited vendor material does not provide a standardized head-to-head benchmark across Tesla, NVIDIA, Mobileye and Qualcomm using matched workloads, precision, memory, power and cooling conditions. NVIDIA’s up-to-254-TOPS Orin figure is a published SoC maximum; Mobileye’s stated multiples compare EyeQ generations within its own family. Neither can be fairly treated as a common score against Tesla’s custom computer or Qualcomm’s platform description.
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What to compare when evaluating an automaker’s system
For a meaningful comparison, look beyond a chip label or peak compute number. Ask whether the published figure describes a chip, a complete computer or a wider platform, and check:
- Intended role: core ADAS, premium driver assistance or a broader centralized vehicle computer.
- Automation and driver responsibility: what the system is designed to do and whether a driver must supervise it.
- Inputs and redundancy: which sensors feed the system and how the overall vehicle design handles failures.
- Compute context: chip generation and configuration, numerical precision, workload, memory and bandwidth.
- Operating limits: power, cooling and thermal conditions that affect sustained operation.
- Software and safety: model compatibility, integration, safety architecture and cybersecurity approach.
- Deployment status: whether the automaker and platform combination is in a shipped vehicle, or remains an announced plan; also check market and region.
Supplier platform families are not interchangeable, and Tesla’s integrated design is not directly comparable to a single supplier chip specification. The available vendor figures describe selected features and positioning, not which vehicle system performs best on the road.
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