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NVIDIA GB10 Grace Blackwell (DGX Spark)
NVIDIA · APU · Grace Blackwell

NVIDIA GB10 Grace Blackwell (DGX Spark) — Benchmarks & Specs

2025

Short answer: the NVIDIA GB10 Grace Blackwell (DGX Spark) generates 60.6 tokens/sec on gpt-oss:120b (MXFP4) under llama.cpp in a single cited run, per llama.cpp GitHub discussion #16578; there are too few comparable runs on file for a median.

Bottom line: how fast is the NVIDIA GB10 Grace Blackwell (DGX Spark)?

For local LLM inference it generates 60.6 tokens/sec running gpt-oss:120b at MXFP4 under llama.cpp (the source did not report the machine's memory configuration; 59 GB peak use), per llama.cpp GitHub discussion #16578. Unified memory is the binding constraint for local inference: the NVIDIA GB10 Grace Blackwell (DGX Spark) ships in configurations of up to 128 GB, per the manufacturer spec sheet, and a 120B model at MXFP4 needs roughly 73 GB for its weights alone (120B parameters at ~4.85 bits per weight, per llama.cpp), so it runs only on configurations with more unified memory than that. In Geekbench 6 multi it scores 18,183 points, per daniel.lawrence.lu.

Every figure above is a row in the tables below, and each row links out to the review or public benchmark database the number was taken from; the memory ceiling and the weight-size estimate are not table rows and link to their sources inline. SpecPicks aggregates published measurements; it does not report first-party benchmark runs.

The NVIDIA GB10 Grace Blackwell (DGX Spark) is an APU from the Grace Blackwell family released in 2025 from NVIDIA. Key on-paper specs include 20 CPU cores, up to 128 GB of unified memory, 273 GB/s of memory bandwidth. Data on this page draws on 2 synthetic benchmark results, 12 community AI inference reports, compiled from JetsonHacks, NVIDIA Developer Blog, LMSYS Org blog, daniel.lawrence.lu, llama.cpp GitHub discussion #16578; each table row links to its source. Read this page when shopping the NVIDIA GB10 Grace Blackwell (DGX Spark), comparing it against other APUs in your build, or sizing it for a specific workload (synthetic benchmark scores or local LLM inference).

AI Inference Performance

Tokens per second under each model + quantization. Higher = faster generation. Bars compare runs across the same model.

Local LLM inference throughput on the NVIDIA GB10 Grace Blackwell (DGX Spark), in generated tokens per second for a single request (one user's generation speed). Higher is better; each row links to the community report or benchmark database it came from. Batched serving runs are listed separately below.
Model Quantization Relative Tokens/sec VRAM used Source
gpt-oss:20b MXFP4 llama.cpp 82.7 tok/s — NVIDIA Developer Blog 2025-10-24
gemma3:4b Q4_0 llama.cpp 79.8 tok/s 2.4 GB JetsonHacks (llama.cpp discussion #16578 results) 2025-10-15
gpt-oss:20b MXFP4 llama.cpp 79.7 tok/s 11.3 GB JetsonHacks (llama.cpp discussion #16578 results) 2025-10-15
gpt-oss:120b MXFP4 llama.cpp 60.6 tok/s 59.0 GB llama.cpp GitHub discussion #16578 2025-11-02
qwen3-coder:30b-a3b q8_0 llama.cpp 60.0 tok/s 30.3 GB JetsonHacks (llama.cpp discussion #16578 results) 2025-10-15
gpt-oss:120b MXFP4 llama.cpp 55.4 tok/s — NVIDIA Developer Blog 2025-10-24
gpt-oss:20b MXFP4 ollama 49.7 tok/s — LMSYS Org blog 2025-10-13
qwen2.5-coder:7b q8_0 llama.cpp 29.4 tok/s 7.5 GB JetsonHacks (llama.cpp discussion #16578 results) 2025-10-15

Batched serving throughput

Aggregate output tokens per second on the NVIDIA GB10 Grace Blackwell (DGX Spark) summed across many concurrent requests (serving runtimes such as vLLM). This is total server throughput, not one user's generation speed — each request generates far slower — so these rows are not comparable with the single-stream table above.
Model Quantization Relative Tokens/sec (aggregate) VRAM used Source
llama3.1:8b NVFP4 TensorRT-LLM batched 38.6 tok/s aggregate — NVIDIA Developer Blog 2025-10-24
qwen3:14b NVFP4 TensorRT-LLM batched 22.7 tok/s aggregate — NVIDIA Developer Blog 2025-10-24
llama3.1:8b FP8 sglang batched 20.5 tok/s aggregate — LMSYS Org blog 2025-10-13
llama3.1:70b FP8 sglang batched 2.7 tok/s aggregate — LMSYS Org blog 2025-10-13

Synthetic Benchmarks

Higher is better. Bars are scaled within each benchmark family (multi-thread, single-thread, etc.) so you can compare like-with-like at a glance.

Synthetic benchmark scores for the NVIDIA GB10 Grace Blackwell (DGX Spark) — higher is better. Each row links to the public database the number was taken from.
Benchmark Relative Score Source
Geekbench 6 multi 18,183 points daniel.lawrence.lu (independent blog) 2026-07-15
Geekbench 6 single 2,983 points daniel.lawrence.lu (independent blog) 2026-07-15

Full Specifications

CPU archGrace (Arm)
GPU archBlackwell
CPU cores20
AI pflops fp41
Memory bandwidth273 GB/s
Max unified memory128 GB

Source: techcrunch.com

NVIDIA GB10 Grace Blackwell (DGX Spark) — Frequently Asked Questions

What is the NVIDIA GB10 Grace Blackwell (DGX Spark) best used for?
NVIDIA GB10 Grace Blackwell (DGX Spark) is a Grace Blackwell-family integrated CPU+GPU. Mainstream desktops and gaming builds are the typical workloads.
When was the NVIDIA GB10 Grace Blackwell (DGX Spark) released?
NVIDIA GB10 Grace Blackwell (DGX Spark) was released in 2025, a Grace Blackwell-generation part.
Where do the NVIDIA GB10 Grace Blackwell (DGX Spark) benchmark numbers come from?
Each benchmark row on this page names its source (public benchmark databases such as TechPowerUp, PassMark, Geekbench and Cinebench, and community reports such as r/LocalLLaMA threads) and links to it, so every number can be checked against the original.
How many cores does the NVIDIA GB10 Grace Blackwell (DGX Spark) have, and how does it benchmark?
Core and thread counts for NVIDIA GB10 Grace Blackwell (DGX Spark) aren't on file. Synthetic benchmark results (PassMark CPU Mark, Cinebench, Geekbench) are in the Synthetic Benchmarks section.
Where can I buy the NVIDIA GB10 Grace Blackwell (DGX Spark)?
SpecPicks has no verified listing of the NVIDIA GB10 Grace Blackwell (DGX Spark) on file yet; the Amazon search link at the top of this page shows its current offers. SpecPicks earns a small affiliate commission on qualifying purchases.

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