Can a DGX Spark GB10 Grace Blackwell, 128GB run local LLMs?

Yes — 39 of the 48 current models on this site fit in its 119.5 GB of usable memory. The strongest it holds, Qwen3.8 27B, runs at 11 tok/s with a 32k window, worked out from memory bandwidth. Whether that saves you money is a different question: at $4,699 and 500k tokens a day, it pays back in 346 years.

Run the numbers on this machine

Jump to: What the DGX Spark, 128GB runs · What has been measured on the DGX Spark, 128GB · Other machines to weigh against the DGX Spark, 128GB · The specifics

What the DGX Spark, 128GB runs

Memory never runs out first here. Every model below holds 256k, where the calculator's list ends, or the longest setting its own context limit allows, so the window you get is the model's choice rather than this machine's.

Speed and memory are at 32k context, the setting the calculator starts on; the memory column is the weights plus the cache for that much of it. Each speed says how it was arrived at, and every one here is estimated from this machine's 273 GB/s of memory bandwidth rather than taken from a published benchmark. There is a page on how that sum works, and what each size needs, and one on what 128 GB runs, machine by machine. The longest context is the longest setting the calculator offers that this machine still holds the model at, cache included, and each one opens the calculator on that model at that length. A figure tagged memory is one this machine ran out of room for, and the rest are stopped by the model's own limit or by the end of the list.

For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. Nothing above reaches it, and the quickest is 70 tok/s.

27 more fit. Ranked below the twelve above: Qwen3.5 9B, Nemotron 3.5 Lightning 30B-A3B, MiniCPM5 2B, Qwen3.5 4B, gpt-oss-120b, Ling 3.0 tiny, Granite 4.2 8B, Mistral Small 4 (119B-2603), Qwen3-Coder 30B-A3B, Qwen3-Coder Next, gpt-oss-20b, Gemma 4 E4B, Devstral 2 123B, LFM2.5 2.6B, Devstral Small 2 24B, Ministral 3 14B, Ministral 3 8B. The other 10 have no intelligence-index score, so the ranking cannot place them: Laguna XS 2.1, LFM3 1.7B, Ornith 1.5 9B, Ornith 1.5 35B-A3B, Ornith 1.5 122B, Spark-X2.5 4B, Spark-X2.6 4B, KAT-Coder V2.5 Dev 35B-A3B, LFM2.5 24B-A2B, Flint 40B-A2.5B. Their pages show what each one needs and what runs it.

The 48 counted above are the current models. This site prices 16 superseded ones as well, because people still run them, and this machine holds 15 of those at 32k, strongest first: GLM-4.5-Air, Qwen3 30B-A3B Instruct 2507, DeepSeek-R1-Distill-Qwen-32B, DeepSeek-R1-Distill-Llama-70B, Llama 3.3 70B Instruct, Llama 3.1 8B Instruct at Q4_K_M, Llama 3.1 8B Instruct at Q8_0, Mistral Small 3.2 24B Instruct, Qwen3 32B at Q4_K_M, Qwen3 32B at Q8_0, Qwen3 14B, Llama 4 Scout 17B-16E, Qwen3 8B, Gemma 3 27B it, Gemma 3 12B it. Each of those pages asks whether the model is still worth running.

What has been measured on the DGX Spark, 128GB

Not one of the speeds above was measured on this machine, and that is not because nobody has run a model on one. This site holds seven published speeds taken on this machine, four of them on superseded models the table above leaves out. They are below, as published and again at 32k of context, which is what every other speed on this page is taken at.

A published figure is what its source reports, at the window the model was run at. The 32k figure is that measurement read at 32k of context: every token generated reads the weights and the whole key-value cache, so the longer the window the slower it runs. The figures come from ggml-org/llama.cpp, DandinPower/llama.cpp_bench, jetsonhacks.com.

Other machines to weigh against the DGX Spark, 128GB

Every row uses the same defaults as the figures above: 500k tokens a day at 15:1 input to output, 32k context, and each machine's strongest model that fits, counted against the same 48 current models. The GeForce RTX 5090, 32GB is priced as the card alone, so add the PC around it before comparing it with a complete computer. All seven cards here are ranked by what each one holds.

Head to head with another computer: Mac mini M5 Pro, 24GB · Mac Studio M5 Max, 128GB · GMKtec EVO-X2, 128GB · MacBook Air M5 (15-inch), 16GB · MacBook Pro M5 Pro (16-inch), 64GB · Corsair AI Workstation 300, 128GB. With a graphics card: RTX PRO 6000 Blackwell, 96GB · Radeon AI PRO R9700, 32GB. There are 111 machine match-ups on the site.

The specifics

Chip

GB10 Grace Blackwell — Founders Edition, 4TB

Memory bandwidth

273 GB/s

Usable by the GPU

119.5 GB — CUDA deviceQuery reports 122,570 MiB (about 119.7 GiB) visible to the GPU under DGX OS; treated as 119.5 GB usable.

Power under load

150 W (measured by a third party) — NVIDIA publishes no system draw (240 W supply, GB10 TDP 140 W). 150 W is a single third-party unit's peak under inference (kubesimplify); Jeff Geerling measured 122.8 W running gpt-oss-20b.

Availability

Shipping. MSRP raised from $3,999 to $4,699 in February 2026. Partner boxes (ASUS, Dell, Lenovo, Acer) exist but none was verifiably cheaper and in stock when checked.

Sources

NVIDIA, NVIDIA Developer Forums, Kubesimplify, geerlingguy/ai-benchmarks