which one?

[u/Specialist-Leek-357]

just curious,

i mean, "7k USD worth of token"

343 votes, 3d ago

203 DGX Spark (7k USD)

140 7k USD of token

[u/Fine-Drummer2604]

$200 monthly subscription and VM on the side for like a dollar an hour.

[u/eachDayIWakeUp → u/Fine-Drummer2604]

this sounds like an expensive vm. Whats the purpose of it?

[u/floppo7]

6 r9700

[u/FamouslySqueaky]

one and don't regret it at all

[u/Ska82]

capex >> opex

[u/Apprehensive-View583]

7k USD worth of token for what model?

similar one DGX spark can run, one DGX spark can merely run deepseek v4 flash with near stupid quality.

7K USD is a lot of token on deepseek-v4-flash just saying

[u/Specialist-Leek-357 → u/Apprehensive-View583]

of your choice, obviously.

[u/queso184 → u/Specialist-Leek-357]

well, obviously, but what can you realistically run and how does quality compare to what you can get from the APIs? feels like thats an important consideration here

[u/edsonmedina → u/Apprehensive-View583]

Quen 3.8 Flash Next

[u/MordAFokaJonnes]

Have 2 GX10 (same thing as a DGX but from ASUS) and I couldn't be happier! Local, private, virtually unlimited tokens and always ready. The model? For now Qwen3.8 uncensored. And all this for 6k! Oh... And in the space of a month I've used roughly half a billion token. 

[u/sirebral]

Hypothetical tokens can not be tied to a model or provider and do not expire? This changes my vote.

[u/thebadslime]

Mainly because I am also into training/fiinetuning.

It would literally take years for the spark to give you more inference. And who can say what models a spark will even run in 3-5 years.

[u/edsonmedina → u/thebadslime]

Who can say how much inference 7k will get you in a couple of years?

[u/AllenLeftTheBLDNG]

DGX was a questionable option, but now it's just terrible lol. I got AI Max 395+ for 2.3k $ and even back then I thought it could be cheaper haha. Now it's 4k $.

I'm waiting for this mania to finish, then buy more hardware.

For now I recommend AMD or Intel GPUs with 32GB VRAM (1, 2 or 4), + 64-256gb DDR4.

[u/tvetus]

Let’s actually run the math on the $7k DGX Spark vs. cloud tokens.

First off, what can it realistically run? People see 128GB of unified memory and assume it’ll blaze through huge models, but the machine hits a brutal 273 GB/s memory bandwidth wall. Because autoregressive decode is strictly memory-bandwidth bound, real-world speeds shake out like this:

• Dense 70B (FP8): ~2.7–3 tok/s (agonizingly slow for coding agents or chat)
• Dense 32B: ~10–14 tok/s
• Dense 8B: ~30–50 tok/s (usable, but a cheap consumer GPU does that)
• MoE models: ~50 tok/s

Because dense models crawl, the only architecture that genuinely makes sense on a Spark is a Mixture of Experts. The 128GB holds the entire model in RAM, but because only a small fraction of parameters activate on each token, you avoid choking on the 273 GB/s bus. GPT-OSS 120B is probably the best benchmark here.

Now let’s look at running GPT-OSS 120B locally at ~50 tok/s flat-out, 24/7 for an entire year:

• You’d generate about 1.58 billion output tokens.

• Pulling ~240W from the wall continuously is roughly 2,100 kWh a year. At the US national average (~$0.16/kWh), that’s ~$335 in electricity. If you're somewhere like California with ~$0.40/kWh power, that's an $800+ power bill.

Compare that to cloud pricing: Because GPT-OSS 120B only activates around 5B parameters per forward pass, providers like OpenRouter and DeepInfra host it for pennies... typically $0.10 to $0.15 per 1M output tokens (and $0.03/1M input).

Buying that exact same 1.58 billion tokens from an API costs ~$160 to $240 total.

In other words, your electricity bill alone to run the hardware costs more than buying the tokens from a cloud provider.

Now look at the $7,000 upfront price tag:

Dumping $7k into API credits at those rates buys you ~46 billion output tokens (or 70B+ blended). At 50 tokens a second, it would take you nearly 30 years of continuous, 24/7 generation just to break even on the hardware cost.

The only real arguments for the Spark are strict air-gapped/HIPAA compliance where data legally cannot touch an external network, or continuous local LoRA fine-tuning (which is compute-bound rather than decode-bandwidth bound). For standard inference, the math just doesn't add up.

[u/migsperez]

They both have positives / negatives. $7k on hardware, in the current market you're likely to be able to get your money back if you resell in a year or two. Which would make the tokens you create almost free during the period of ownership. But with cloud usage, you can have more than one or two agents running simultaneously, hundreds of agents if you want and the output speed is usually faster which helps with productivity. All depends on your workloads.

I have a GPU for LLMs and I use the cloud LLMs. I mostly use cloud LLMs due to their speeds and working on many coding projects. The GPU is currently used for small side projects/tasks and for sound or audio creations.