NVIDIA · Board / edge

Jetson T4000 (64GB)

Compact Blackwell edge module, industrial AI

  • 64 GBunified memory
  • 70Bfits in memory (Q4, 8k)
  • 32Bpractical (estimate)
  • 70 Wmax draw
  • ~€1,999indicative price

What can it run?

Pick a job or a model size. The bar compares what it needs with this machine's usable memory.

  • Model weights
  • Context cache
  • Runtime + extras (Whisper, embeddings)

How we calculate this

Weights = parameters × bits per weight / 8 (Q4_K_M ≈ 4.85; Q8_0 ≈ 8.5). Cache = context tokens × the reference model's per-token size (layers × KV heads × head size × 2 × 2 bytes). Plus 1.5 GB of runtime. Usable memory = total minus what the system keeps (3–6 GB on shared memory, 0.5 GB on a GPU). Comfortable = fits in 85%. It is a ±20% estimate: measure it on your machine before you decide. How to pick the GGUF file

When does it beat the cloud?

Set your usage. We compare the purchase plus electricity with what you would pay a cloud API.

–electricity / month
–cloud API / month
–to pay for itself
Cumulative cost by month
MonthOwn hardwareCloud
  • Own hardware (purchase + electricity)
  • Cloud API

Assumptions: €0.25/kWh (Spain average, editable), USD 1 = €0.92, 3 input tokens per output token, and the machine at maximum draw for every hour it is on (worst case). API prices verified 2026-09-09. Your time and maintenance are not included. Note: the cloud side is a frontier model and here you would run a smaller open one; this compares cost, not quality.

Learn with this machine

Skip it if…

  • a cloud API already covers you and your volume is low: check the break-even maths.

Specs and where the numbers come from

CPUArm Neoverse (Blackwell)
GPUBlackwell SM
NPU1200 TFLOPS FP4
Memory64 GB (unified memory); usable by the model ≈ 58 GB
Speed60 tok/s with 32B FP16 vendor or community estimate, not measured by us
Price~€1,999 indicative, checked 2026-09-19; check the live price in the shop

We only call something "measured" when it ran on our machines. Editorial policy