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GB10 / DGX Spark: CUDA-Native Desktop AI
GB10 is NVIDIA's Grace Blackwell superchip: a 20-core Arm CPU fused to a Blackwell GPU with 128GB of coherent unified memory, CUDA-native. The DGX Spark and ASUS Ascent GX10 both ship it, with a 200GbE ConnectX-7 NIC so two units cluster to run 405B-class models. Memory bandwidth is 273 GB/s — a similar ballpark to Strix Halo — so what you're really buying here is CUDA and capacity, not bandwidth.
Top Picks
NVIDIA DGX Spark (GB10 Grace Blackwell)
$4,699+
- Chip: GB10 Grace Blackwell Superchip
- CPU: 20-core Arm (10× Cortex-X925 + 10× A725)
- GPU: Blackwell (5th-gen Tensor Cores), CUDA-native
ASUS Ascent GX10 (GB10 Grace Blackwell)
$2,999 – $4,100
- Chip: GB10 Grace Blackwell Superchip
- CPU: 20-core Arm (10× Cortex-X925 + 10× A725)
- GPU: Blackwell (5th-gen Tensor Cores), CUDA-native
Comparisons
Models That Run on These Boxes
Llama 3.3 70B Instruct
Meta's flagship dense chat model — strong general reasoning, coding, and instruction-following that rivals much larger models. The default pick when a single big box has the memory to spare.
LlamaLlama 4 Scout
A mixture-of-experts model — only a fraction of its 109B total parameters are active per token, so it loads like a large model but runs far faster than its size suggests. A natural fit for unified-memory boxes with a long context window.
Qwen3Qwen3 8B
A capable small chat-and-reasoning model that fits comfortably even on budget boxes — a good everyday assistant when you don't need frontier-level depth.
Qwen3Qwen3 14B
The mid-size Qwen3 — noticeably stronger reasoning and coding than the 8B while still fitting mid-range hardware at Q4.
Qwen3Qwen3 32B
Qwen3's large dense model — near-flagship quality that still runs on a single unified-memory box, a strong local alternative to 70B-class models at lower memory cost.
DeepSeek-R1DeepSeek-R1 Distill Qwen 7B
R1's chain-of-thought reasoning distilled into a 7B Qwen base — the lightest way to get R1-style step-by-step reasoning locally, small enough for budget boxes.
DeepSeek-R1DeepSeek-R1 Distill Qwen 14B
The 14B R1 distill — a good balance of reasoning depth and hardware cost, running comfortably on mid-range unified-memory boxes.
DeepSeek-R1DeepSeek-R1 Distill Qwen 32B
The strongest Qwen-based R1 distill — heavy reasoning that still fits a single large-memory box, the sweet spot for local R1-style work.
DeepSeek-R1DeepSeek-R1 Distill Llama 70B
R1's reasoning distilled onto a Llama 70B base — the highest-quality distill, and the one to reach for when the full 671B R1 won't fit (it never does locally).
Gemma 3Gemma 3 4B
Google's smallest current Gemma — tiny memory footprint and multimodal, ideal for the most memory-constrained budget boxes and always-on assistants.
Gemma 3Gemma 3 12B
The mid-size Gemma 3 — solid general-purpose quality with a long context window, fitting mid-range hardware at Q4.
Gemma 3Gemma 3 27B
The largest Gemma 3 — frontier-adjacent quality for a dense open model, comfortably runnable on a single large-memory box.
GPT-OSSGPT-OSS 20B
OpenAI's small open-weight MoE — its 21B total parameters load like a mid-size model, but because only a fraction are active per token it runs fast on modest hardware.
GPT-OSSGPT-OSS 120B
OpenAI's large open-weight MoE — its 117B total parameters fit a single high-memory box, and because only a fraction are active per token it runs far quicker than its total suggests.
PhiPhi-4
Microsoft's 14B model punches well above its size on math and reasoning — a compact, mid-range-friendly pick when you want strong reasoning without a big memory bill.
MistralMistral Small 3.2 24B
Mistral's 24B dense model — fast, capable, and low-latency for its class, sitting neatly between the mid-size and large boxes.
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ReadFrequently Asked Questions
What is the GB10 / DGX Spark?
GB10 is NVIDIA's Grace Blackwell superchip — a 20-core Arm CPU paired with a Blackwell GPU and 128GB of coherent unified memory, running the full CUDA stack. Both the NVIDIA DGX Spark and the ASUS Ascent GX10 are built on it.
How is GB10 different from a Strix Halo box?
CUDA. GB10 runs NVIDIA's native software stack, so tools that assume CUDA work out of the box. Memory bandwidth is 273 GB/s — a similar ballpark to Strix Halo — so the differentiator is the software ecosystem and capacity, not raw bandwidth.
DGX Spark or ASUS Ascent GX10?
Both use the same GB10 silicon, 128GB unified memory, 273 GB/s bandwidth, and ConnectX-7 200GbE. The Ascent GX10 undercuts the DGX Spark on SSD tiers ($2,999–$4,100) and ships more widely; the DGX Spark ($4,699+) carries NVIDIA's DGX OS and brand.
Can I run 405B-class models on GB10?
Not on a single box, but two units cluster over the 200GbE ConnectX-7 NIC to reach 405B-class models locally.