Other unified memory
What AI models can the NVIDIA DGX Spark (GB10) run locally?
At 4-bit quantization with an 8K-token conversation, 84 of the 146 open-weight models in our directory fit entirely in the NVIDIA DGX Spark (GB10)'s 128 GB of unified memory (about 96 GB usable, the default GPU share, often adjustable), up to Ling 3.0 Flash. Among models of 7B parameters or more, Laguna XS 2.1 (free) is the fastest, at an estimated 104 to 135 tokens per second.
| Model | Parameters | Memory needed | Estimated speed |
|---|---|---|---|
| Runs entirely in fast memory (84) | |||
| Ling 3.0 Flash Inclusionai | 127B 8.6B active | 78 GB 77 GB + 0.4 GB cache | 30–39 tokens/s Fast |
| Ling 3.0 Flash Inclusionai | 127B 8.6B active | 78 GB 77 GB + 0.4 GB cache | 30–39 tokens/s Fast |
| Qwen3.5 122B-A10B Alibaba (Qwen) | 125B 12.7B active | 77 GB 76 GB + 0.2 GB cache | 21–27 tokens/s Comfortable to read |
| Ling 3.0 Flash VL Inclusionai | 125B 5.9B active | 77 GB 76 GB + 0.4 GB cache | 43–55 tokens/s Fast |
| Ling 3.0 Flash VL Inclusionai | 125B 5.9B active | 77 GB 76 GB + 0.4 GB cache | 43–55 tokens/s Fast |
| Nemotron Super NVIDIA | 124B | 76 GB 75 GB + 0.1 GB cache | Speed not estimated for this architecture |
| Laguna S 2.1 (free) Poolside | 118B 6.1B active | 73 GB 71 GB + 0.5 GB cache | 41–53 tokens/s Fast |
| OpenAI GPT 120B OpenAI | 117B 5.8B active | 72 GB 71 GB + 0.3 GB cache | 45–58 tokens/s Fast |
| GLM-4.5-Air Zhipu AI (Z.ai) | 110B 17.0B active | 69 GB 67 GB + 1.5 GB cache | 15–19 tokens/s Comfortable to read |
| Llama 4 Scout Meta | 109B | 67 GB weights only | Speed not estimated (architecture not public) |
| GLM-4.6V Zhipu AI (Z.ai) | 108B 14.3B active | 68 GB 65 GB + 1.5 GB cache | 17–22 tokens/s Comfortable to read |
| GLM-4.5V Zhipu AI (Z.ai) | 108B 14.3B active | 68 GB 65 GB + 1.5 GB cache | 17–22 tokens/s Comfortable to read |
| Qwen3-Next 80B-A3B Instruct Alibaba (Qwen) | 81.3B 5.5B active | 51 GB 49 GB + 0.8 GB cache | 43–56 tokens/s Fast |
| Qwen3-Next 80B-A3B (Thinking) Alibaba (Qwen) | 81.3B 5.5B active | 51 GB 49 GB + 0.8 GB cache | 43–56 tokens/s Fast |
| Hunyuan A13B Instruct Tencent (Hunyuan) | 80.4B | 51 GB 49 GB + 1.1 GB cache | Speed not estimated for this architecture |
| Qwen3 Coder Next Alibaba (Qwen) | 79.7B 3.9B active | 50 GB 48 GB + 0.8 GB cache | 59–76 tokens/s Fast |
| Qwen2.5-VL 72B Instruct Alibaba (Qwen) | 73.4B | 48 GB 45 GB + 2.7 GB cache | 3.5–4.6 tokens/s Usable but slow |
| Qwen2.5 72B Instruct Alibaba (Qwen) | 72.7B | 48 GB 44 GB + 2.7 GB cache | 3.6–4.6 tokens/s Usable but slow |
| Magnum v4 72B Anthracite ORG | 72.7B | 48 GB 44 GB + 2.7 GB cache | 3.6–4.6 tokens/s Usable but slow |
| Hermes 3 70B Instruct Nousresearch | 70.6B | 46 GB 43 GB + 2.7 GB cache | 3.7–4.8 tokens/s Usable but slow |
| Hermes 3 70B Instruct Nousresearch | 70.6B | 46 GB 43 GB + 2.7 GB cache | 3.7–4.8 tokens/s Usable but slow |
| Llama-3.3-70B-Instruct Meta | 70.6B | 44 GB weights only | Speed not estimated (architecture not public) |
| Llama-3.1-70B-Instruct Meta | 70.6B | 44 GB weights only | Speed not estimated (architecture not public) |
| Skyfall 36B V2 Thedrummer | 36.9B | 25 GB 22 GB + 2.1 GB cache | 6.9–9.0 tokens/s Usable but slow |
| Qwen3.5 35B-A3B Alibaba (Qwen) | 36.0B 4.7B active | 23 GB 22 GB + 0.2 GB cache | 55–71 tokens/s Fast |
| Qwen3.6 35B-A3B Alibaba (Qwen) | 36.0B 4.7B active | 23 GB 22 GB + 0.2 GB cache | 55–71 tokens/s Fast |
| Nex-N2.5-Mini NEX AGI | 35.1B 3.9B active | 22 GB 21 GB + 0.2 GB cache | 66–86 tokens/s Fast |
| Laguna XS 2.1 (free) Poolside | 33.4B 2.2B active | 21 GB 20 GB + 0.4 GB cache | 104–135 tokens/s Fast |
| Qwen3 VL 32B Instruct Alibaba (Qwen) | 33.4B | 23 GB 20 GB + 2.1 GB cache | 7.6–9.9 tokens/s Usable but slow |
| Qwen2.5 Coder 32B Instruct Alibaba (Qwen) | 32.8B | 23 GB 20 GB + 2.1 GB cache | 7.8–10 tokens/s Usable but slow |
| Qwen3 32B Alibaba (Qwen) | 32.8B | 23 GB 20 GB + 2.1 GB cache | 7.8–10 tokens/s Usable but slow |
| Nemotron 3 Nano 30B A3B NVIDIA | 31.6B | 20 GB 19 GB + 0.1 GB cache | Speed not estimated for this architecture |
| Nemotron 3.5 Lightning 30B A3B NVIDIA | 31.6B | 20 GB 19 GB + 0.4 GB cache | Speed not estimated for this architecture |
| Gemma 4 31B IT Google DeepMind | 31.3B | 22 GB 19 GB + 2.2 GB cache | 8.1–10 tokens/s Usable but slow |
| GLM-4.7-Flash Zhipu AI (Z.ai) | 31.2B 5.2B active | 20 GB 19 GB + 0.4 GB cache | 48–63 tokens/s Fast |
| Qwen3 VL 30B A3B Instruct Alibaba (Qwen) | 31.1B 3.9B active | 20 GB 19 GB + 0.8 GB cache | 59–76 tokens/s Fast |
| Qwen3 VL 30B A3B Thinking Alibaba (Qwen) | 31.1B 3.9B active | 20 GB 19 GB + 0.8 GB cache | 59–76 tokens/s Fast |
| Qwen3-Coder 30B-A3B Instruct Alibaba (Qwen) | 30.5B 3.4B active | 20 GB 19 GB + 0.8 GB cache | 67–86 tokens/s Fast |
| Qwen3 30B A3B Instruct 2507 Alibaba (Qwen) | 30.5B 3.4B active | 20 GB 19 GB + 0.8 GB cache | 67–86 tokens/s Fast |
| Qwen3 30B A3B Thinking 2507 Alibaba (Qwen) | 30.5B 3.4B active | 20 GB 19 GB + 0.8 GB cache | 67–86 tokens/s Fast |
| Qwen3 30B A3B Alibaba (Qwen) | 30.5B 3.4B active | 20 GB 19 GB + 0.8 GB cache | 67–86 tokens/s Fast |
| Hy-MT2-30B-A3B Tencent (Hunyuan) | 30.1B 3.5B active | 20 GB 18 GB + 0.8 GB cache | 65–84 tokens/s Fast |
| Muse Glimmer 30B Meta | 29.8B | 19 GB 18 GB + 0.2 GB cache | 9.0–12 tokens/s Comfortable to read |
| Qwen3.8 27B Alibaba (Qwen) | 27.8B | 18 GB 17 GB + 0.5 GB cache | 9.5–12 tokens/s Comfortable to read |
| Qwen3.5 27B Alibaba (Qwen) | 27.8B | 18 GB 17 GB + 0.5 GB cache | 9.5–12 tokens/s Comfortable to read |
| Qwen3.6 27B Alibaba (Qwen) | 27.8B | 18 GB 17 GB + 0.5 GB cache | 9.5–12 tokens/s Comfortable to read |
| Gemma 3 27B IT Google DeepMind | 27.4B | 17 GB weights only | Speed not estimated (architecture not public) |
| Gemma 2 27B Google DeepMind | 27.2B | 17 GB weights only | Speed not estimated (architecture not public) |
| Gemma 4 26B A4B IT Google DeepMind | 25.8B | 17 GB 16 GB + 0.5 GB cache | Speed not estimated for this architecture |
| Voxtral Small 24B 2507 Mistral AI | 24.3B | 17 GB 15 GB + 1.3 GB cache | 11–14 tokens/s Comfortable to read |
| Uncensored Cognitivecomputations | 24.0B | 17 GB 15 GB + 1.3 GB cache | 11–14 tokens/s Comfortable to read |
| Mistral Small 3.1 24B Instruct Mistral AI | 24.0B | 17 GB 15 GB + 1.3 GB cache | 11–14 tokens/s Comfortable to read |
| LFM2.5-24B-A2B Liquid | 24.0B | 16 GB 15 GB + 0.2 GB cache | Speed not estimated for this architecture |
| Mistral Small 3 Mistral AI | 23.6B | 16 GB 14 GB + 1.3 GB cache | 11–14 tokens/s Comfortable to read |
| Cydonia 24B V4.1 Thedrummer | 23.6B | 16 GB 14 GB + 1.3 GB cache | 11–14 tokens/s Comfortable to read |
| Cydonia 24B V4.1 Thedrummer | 23.6B | 16 GB 14 GB + 1.3 GB cache | 11–14 tokens/s Comfortable to read |
| Safety GPT OSS 20B OpenAI | 21.5B 4.8B active | 14 GB 13 GB + 0.2 GB cache | 54–70 tokens/s Fast |
| GPT OSS 20B OpenAI | 20.9B 4.2B active | 14 GB 13 GB + 0.2 GB cache | 61–79 tokens/s Fast |
| Reka Flash 3 Rekaai | 20.9B | 15 GB 13 GB + 1.1 GB cache | 12–16 tokens/s Comfortable to read |
| Qwen3 14B Alibaba (Qwen) | 14.8B | 11 GB 9.0 GB + 1.3 GB cache | 17–22 tokens/s Comfortable to read |
| Phi 4 Microsoft | 14.7B | 11 GB 8.9 GB + 1.7 GB cache | 17–22 tokens/s Comfortable to read |
| Ministral 3 14B Mistral AI | 13.9B | 11 GB 8.5 GB + 1.3 GB cache | 18–23 tokens/s Comfortable to read |
| Gemma 3 12B IT Google DeepMind | 12.2B | 8.2 GB weights only | Speed not estimated (architecture not public) |
| Llama Guard 4 12B Meta | 12.0B | 8.1 GB weights only | Speed not estimated (architecture not public) |
| Qwen3.5 9B Alibaba (Qwen) | 9.7B | 6.9 GB 5.9 GB + 0.3 GB cache | 27–35 tokens/s Fast |
| Moondream 3.1 Moondream | 9.3B | 6.4 GB weights only | Speed not estimated (architecture not public) |
| Moondream 3 Preview Moondream | 9.3B | 6.4 GB weights only | Speed not estimated (architecture not public) |
| Ministral 3 8B Mistral AI | 8.9B | 7.3 GB 5.4 GB + 1.1 GB cache | 27–35 tokens/s Fast |
| Granite 4.2 8B IBM | 8.8B | 7.5 GB 5.3 GB + 1.3 GB cache | 27–35 tokens/s Fast |
| Qwen3 VL 8B Instruct Alibaba (Qwen) | 8.8B | 7.3 GB 5.3 GB + 1.2 GB cache | 27–36 tokens/s Fast |
| Qwen3 VL 8B Thinking Alibaba (Qwen) | 8.8B | 7.3 GB 5.3 GB + 1.2 GB cache | 27–36 tokens/s Fast |
| UI-TARS 7B Bytedance | 8.3B | 6.3 GB 5.0 GB + 0.5 GB cache | 31–40 tokens/s Fast |
| Qwen3 8B Alibaba (Qwen) | 8.2B | 7.0 GB 5.0 GB + 1.2 GB cache | 29–38 tokens/s Fast |
| Llama 3.1 8B Instruct fp8 Meta | 8.0B | 5.7 GB weights only | Speed not estimated (architecture not public) |
| Hy-MT2-7B Tencent (Hunyuan) | 8.0B | 6.7 GB 4.9 GB + 1.1 GB cache | 30–39 tokens/s Fast |
| Qwen2.5 7B Instruct Alibaba (Qwen) | 7.6B | 5.9 GB 4.6 GB + 0.5 GB cache | 33–43 tokens/s Fast |
| Gemma 3 4B IT Google DeepMind | 4.3B | 3.4 GB weights only | Speed not estimated (architecture not public) |
| Nemotron 3.5 Content Safety (free) NVIDIA | 4.3B | 3.7 GB 2.6 GB + 0.3 GB cache | 59–76 tokens/s Fast |
| Ministral 3 3B Mistral AI | 3.8B | 4.0 GB 2.3 GB + 0.9 GB cache | 59–76 tokens/s Fast |
| Llama 3.2 3B Instruct Meta | 3.2B | 2.7 GB weights only | Speed not estimated (architecture not public) |
| Granite 4.0 Micro IBM | 3.2B | 2.8 GB 1.9 GB + 0.1 GB cache | 83–107 tokens/s Fast |
| Hy-MT2-1.8B Tencent (Hunyuan) | 2.0B | 2.6 GB 1.2 GB + 0.5 GB cache | 108–140 tokens/s Fast |
| Hy-MT2-1.8B Tencent (Hunyuan) | 2.0B | 2.6 GB 1.2 GB + 0.5 GB cache | 108–140 tokens/s Fast |
| Llama 3.2 1B Instruct Meta | 1.2B | 1.5 GB weights only | Speed not estimated (architecture not public) |
62 larger models do not fit. Change the quantization, conversation length or memory size in the tool.
Compared with similar chips
- Apple M5 Pro: 64 GB, 68 models fit entirely (-16); about 11% faster on models that fit both (307 vs 273 GB/s).
- Apple M5 Max (40-core GPU): same memory, so the same models fit; about 57% faster on models that fit both (614 vs 273 GB/s).
- Apple M4 Max (16-core CPU): same memory, so the same models fit; about 48% faster on models that fit both (546 vs 273 GB/s).
- AMD Ryzen AI Max+ 395: same memory, so the same models fit; about 6% slower on models that fit both (256 vs 273 GB/s).
- Apple M3 Ultra: 512 GB, 119 models fit entirely (+35); about 82% faster on models that fit both (819 vs 273 GB/s).
Frequently asked questions
What is the largest AI model the NVIDIA DGX Spark (GB10) can run?
At 4-bit quantization with an 8K-token conversation, the largest model in our directory that fits entirely in its 128 GB of unified memory is Ling 3.0 Flash (78 GB).
Can the NVIDIA DGX Spark (GB10) run GPT OSS 20B?
Yes. At 4-bit, GPT OSS 20B needs about 14 GB and fits entirely in the NVIDIA DGX Spark (GB10)'s memory at an estimated 61 to 79 tokens per second.
Can the NVIDIA DGX Spark (GB10) run Qwen3 32B?
Yes. At 4-bit, Qwen3 32B needs about 23 GB and fits entirely in the NVIDIA DGX Spark (GB10)'s memory at an estimated 7.8 to 10 tokens per second.
Can the NVIDIA DGX Spark (GB10) run OpenAI GPT 120B?
Yes. At 4-bit, OpenAI GPT 120B needs about 72 GB and fits entirely in the NVIDIA DGX Spark (GB10)'s memory at an estimated 45 to 58 tokens per second.
These are estimates, not measurements: memory from each model's parameter count and published architecture, speed from memory bandwidth, calibrated on the AIPCs test bench. Drivers, software and prompt length move real results. How the estimate works.