DGX Spark vs Ryzen AI Max+ 395

The short answer

NVIDIA DGX Spark and Framework Desktop are not variations on one machine. NVIDIA DGX Spark runs on the NVIDIA GB10 Grace Blackwell and offers a model 120GB at 273 GB/s; Framework Desktop runs on the Ryzen AI Max+ 395 and offers 48GB at 256 GB/s. At today's verified prices, Framework Desktop is the cheaper of the two at $1,959.

NVIDIA DGX Spark holds the larger model. NVIDIA DGX Spark answers faster on anything that fits both. If those two sentences point at different machines, the decision is entirely about the size of the model you intend to run.

Which is better for local LLMs?

NVIDIA DGX Spark, scoring 55.3 against 37.9 on our Local LLM ranking. That ranking weights usable AI memory at 30 percent, bandwidth at 25 and model compatibility at 20, because those three decide whether a model loads and how quickly it replies.

NVIDIA DGX Spark gives a model 120GB against 48GB, which is the difference between the two that matters most at this size. Memory decides what runs at all; everything else decides how pleasant it is.

Which is better for image and video generation?

NVIDIA DGX Spark, at 55.3 against 26.4 on the Image AI ranking. Diffusion models are compute-bound rather than memory-bound, so this ranking leans on raw GPU throughput where the local LLM ranking leans on memory. It is common for the two rankings to disagree, and on this page they agree.

Which is better for agents?

NVIDIA DGX Spark, at 60.5 against 40.7. An agent keeps a long context alive and often holds more than one model resident, so the Agents ranking weights memory higher than any other view, at 35 percent. A machine that generates quickly but runs out of room mid-task is less useful than a slower one that never unloads.

How much memory does each one really give a model?

This is where the two machines separate, and where the number on the box misleads. NVIDIA DGX Spark is a unified coherent design, so a model gets 120GB. Framework Desktop is a unified apu design, giving 48GB. Those figures are computed from the memory architecture, not copied from a marketing specification, and the rule for each architecture is published on our AI memory page.

Work out the largest model you intend to run before comparing anything else. A 32B model at Q4 needs roughly 24GB, a 70B model about 51GB, and a 120B class model about 86GB. Whichever of these two machines clears your number is the machine you should buy, because a model that does not fit does not run at any speed.

How fast will each one answer?

Generating a token means reading every active parameter from memory, so generation speed tracks memory bandwidth closely. NVIDIA DGX Spark reads at 273 GB/s and Framework Desktop at 256 GB/s. On a model that fits both machines, the faster of the two will produce text in roughly that proportion.

We do not publish a tokens per second figure for either machine, because we have not measured one. A rate quoted without the runtime, the quantisation, the context length and the thermal state is worse than no figure at all, so where there is no benchmark on record the system page says performance not yet benchmarked rather than inventing a number.

Which is better value?

Framework Desktop costs less today at $1,959 against $6,950. Value on this site is usable AI memory per dollar rather than the lower sticker, so check the Price and value component on each system page before treating the cheaper machine as the better buy.

What about software support?

Software maturity is worth real money and rarely appears on a spec sheet. CUDA remains the best supported path and most inference and fine-tuning tooling targets it first. Apple's MLX and Metal are well supported and improving quickly. ROCm and Vulkan on Ryzen AI Max work and have improved considerably, but you should still expect occasional friction with tools that assume NVIDIA.

Our Software support component scores exactly this, and it is one of the ten components behind each machine's AIPC Score. If your time is worth more than the price difference between these two systems, weight it more heavily than the raw hardware numbers suggest.

Who should buy which

Buy NVIDIA DGX Spark if the largest model you want to run does not fit 48GB. That is a hard constraint and no amount of extra speed compensates for it.

Buy NVIDIA DGX Spark if your models are comfortably inside both machines' memory and you care how quickly text arrives. Within that range it is the faster machine, and the gap is visible rather than theoretical.

If both machines clear your requirements, take the cheaper one and spend the difference on storage or memory elsewhere. The ranking difference between two systems this close is smaller than the difference a larger model makes.

One caution that applies to both: check the configuration, not just the model name. Manufacturers sell the same badge at several memory sizes, and the cheap configuration is usually the one in stock. Every price on this page names the exact configuration it belongs to, which is why the memory figure and the price in each column always describe the same machine.

How we worked this out

Nothing on this page is an editorial opinion. Each verdict comes from the two machines' stored specifications and their AIPC Scores for the relevant workload, and each score breaks into ten measured components with published weights that you can inspect on either system page.

Prices are read from retailers, timestamped, and refreshed on a schedule. Where an Amazon price is older than 24 hours we remove the number rather than show one we cannot stand behind. Where a machine has no verified price at all we say so, and its value component scores zero rather than being quietly treated as free.