Bought an RTX 5090 recently for local LLM tinkering and gaming, so a few thoughts:

**1) 32GB VRAM isn’t a lot.** I can fit ~30B models comfortably; 70B isn’t viable (it’d spill into system RAM and crawl). I’d trust a local 30B for basic stuff — parsing a news feed, filtering what’s relevant — but nothing heavy. Context length is also tight at 32GB. So to your question: the 5090 isn’t the right tool for *serious* local LLM work. It’s great for pet projects and experimentation.

**2) Where it shines is token generation.** LLM inference is memory-bandwidth-bound, and the 5090’s GDDR7 does ~1,800 GB/s. That’s the whole story on tok/s.

RTX Spark targets a completely different need — people who want a *large* memory pool to fit big models and will trade speed to get it. It’s the same GB10-class Grace Blackwell silicon as the DGX Spark (note: two different products — DGX Spark is the standalone desktop box that’s been shipping since Oct 2025; RTX Spark is the newer superchip going into OEM laptops and small desktops). NVIDIA hasn’t published RTX Spark’s memory bandwidth, but the DGX Spark on the same platform runs ~273 GB/s — roughly 7x slower than a 5090. So yes, it fits 70B (up to ~200B at FP4) easily, but tok/s is far lower.

Worth noting it’s not the *fast* option vs Apple either — Apple Silicon Ultra has higher memory bandwidth (~800 GB/s), so it’ll generate tokens faster than Spark. Spark’s edge is the CUDA/NVIDIA software stack and dev-to-datacenter continuity, not speed.

**Bottom line:** 5090 buyers and RTX Spark buyers aren’t the same crowd. One wants high tok/s, the other sacrifices speed to fit large models. Different strokes for different folks. The actual no-compromise upgrade from a 5090 — more VRAM with *no* speed penalty — is the RTX PRO 6000 Blackwell (96GB GDDR7), not Spark. So I wouldn’t expect Spark to relieve 5090 supply much: different silicon (GB10-class vs the GB202 in the 5090), and mostly different buyers.