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.
| Month | Own hardware | Cloud |
|---|
- 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
- Edge AI Hardware Guide 2026: Jetson vs Mac Mini vs NUCJetson Orin Nano Super, Mac mini M4 and a Core Ultra mini PC compared on vendor specs: memory, TOPS, power and which local models each one can hold.
- AITune: Auto-Tune Local Inference on Your NVIDIA CardWhat NVIDIA AITune really does, when to use it, and the measured Ollama and vLLM settings that speed up a local model on a consumer GPU or a Jetson.
- NPU vs GPU: Why Neural Processing Units Are the Future of Edge AIWhen an NPU is enough for local AI inference and when you still need a GPU: vendor TOPS figures, power draw from spec sheets, and what Ollama actually uses.
- GGUF Quantization: Pick the Right File for Your MachineHow to read a GGUF file name, choose between Q4_K_M, Q5 and Q8 for the memory you have, and measure speed, memory and quality yourself with three commands.
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
| CPU | Arm Neoverse (Blackwell) |
|---|---|
| GPU | Blackwell SM |
| NPU | 1200 TFLOPS FP4 |
| Memory | 64 GB (unified memory); usable by the model ≈ 58 GB |
| Speed | 60 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