Physical AI Engineering Tools

On-Vehicle VLA Compute, Latency & Storage Budget Calculator

Size end-to-end onboard budgets for autonomous driving, off-road vehicles, and mobile robots running Vision-Language-Action (VLA) models—covering inference FLOPs/TOPS, prefill + decode latency, unified memory, onboard SSD storage, monthly telemetry offload, and Vision-only vs. Vision+LiDAR trade-offs.

1. Scenario & Parameter Setup

Select a reference preset or tweak any parameter below to switch to Custom mode.

1. Model Architecture
2. Per-Step Sensor Input (Vision & LiDAR)

Streaming KV reuse caches prior frame tokens so each control step only prefills 1 new frame of camera/LiDAR tokens.

3. Runtime Rate & On-Vehicle SoC
40%

Real-world onboard utilization typically ranges from 30% to 50% due to memory bandwidth limits and kernel launch overhead.

4. Event Logging, Onboard Storage & Offload
50%

2. On-Vehicle Budget Results

On-Vehicle SoC Occupancy --
Single-Step Latency --
Sustained Compute (FP16-eq) --
Onboard Memory (Weights+KV+Act) --
Recommended Onboard SSD Tier --
Monthly Data Offload --
Real-Time Viability Verdict --
Cached Context Tokens --
New Input Tokens / Step --
Total Tokens / Step (incl. Actions) --
Per-Step Inference FLOPs --
Required INT8 TOPS / Selected SoC --
Latency (Prefill / Decode / Total) --
Memory (Weights / KV / Act / Total) --
Event Bitrate / Size per Clip --

3. Vision-Only vs. Vision+LiDAR Comparison (Current Setup)

Toggles only the LiDAR switch while keeping all other parameters fixed to quantify the multimodal overhead across 10 metrics.

Metric Vision-Only Vision + LiDAR Delta (Δ)

* Note: Order-of-magnitude engineering estimator (±2× tolerance). Reserve additional compute margin for deterministic safety-fallback controllers.

On-Vehicle VLA Budget Methodology & Engineering Guide

Deploying a 7B–20B Vision-Language-Action (VLA) policy on an autonomous vehicle or field robot requires balancing four coupled budgets simultaneously: sustained TOPS within the control period, single-step prefill + action decode latency, unified memory for weights and multi-frame GQA KV caches, and fleet event storage/offload bandwidth.

1. Core Sizing Formulas

Forward pass compute is approximated as 2N FLOPs per token, with INT8/FP8 tensor throughput modeled at 2× FP16-equivalent scaled by effective utilization (η_util):

T_ctx = (C_cam × T_cam + I_lidar × T_lidar) × W_win T_new = (C_cam × T_cam + I_lidar × T_lidar) × (1 if KV_reuse else W_win) FLOPs_step ≈ 2 × N × (T_new + T_action) · P_sustained = FLOPs_step × f_Hz TOPS_int8_req = P_sustained ÷ (2 × η_util) · Occupancy = TOPS_int8_req ÷ TOPS_SoC t_step = t_prefill + t_decode = FLOPs_step ÷ (TOPS_SoC × 2 × η_util × 10^12) × 1000 [ms] M_KV = 2 × H_kv × d_head × L × b_kv × T_ctx ÷ 10^9 [GB] · M_total = M_weights + M_KV + M_act

* Weight memory includes a 5% quantization metadata overhead. On-vehicle storage provisions dual A/B OTA model slots (2 × M_weights + 1.5 GB) plus 30 GB for OS and system partitions.

2. Streaming KV Cache Reuse & Multimodal Token Costs

Fusing 6–8 cameras with dense 3D LiDAR tokens across a multi-frame temporal window easily exceeds 10,000 context tokens. Re-prefilling the entire temporal window at 10 Hz would saturate even a 2,000 TOPS SoC. Streaming KV reuse caches historical frame K/V tensors so each control tick only prefills the newly arrived frame.

3. Event-Triggered Ring Buffers & Monthly Fleet Offload

Rather than recording raw sensor streams continuously, production fleets capture 60-second ring-buffer clips around interventions or anomalies, retain them locally for 7–14 days, and upload a curated fraction (e.g., 50%) over Wi-Fi or cellular links.

4. Frequently Asked Questions (FAQ)

Why is sustained FP16-equivalent compute divided by 2 when calculating required INT8 TOPS?

The standard transformer forward approximation (2N FLOPs/token) measures baseline FP16 operations. Because automotive SoC tensor cores execute INT8/FP8 matrix multiplications at roughly 2× the rate of FP16 per clock, dividing by 2 and by the utilization factor yields the required vendor-rated INT8 TOPS.

How much overhead does LiDAR fusion add to an onboard VLA pipeline?

Adding 2,048–4,096 LiDAR tokens per frame typically increases per-step prefill tokens by 35%–60%, directly raising step latency and SoC occupancy. On the data side, raw/compressed LiDAR streams (~0.5 MB/s) also increase per-event clip size and monthly cloud offload volume.

Why should effective hardware utilization be set between 0.30 and 0.50?

Vendor TOPS ratings (such as 2,000 TOPS sparse on DRIVE Thor) represent theoretical peak throughput. In real-time onboard execution, vision patch projection, GQA KV cache memory reads, and serial action token decoding are often memory-bandwidth bound, achieving 30%–50% effective utilization.

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Primary sources

Data & formulas verified: 2026-10-26 · Operator: Huni ([email protected])