NVIDIA DGX Spark (128GB)
The NVIDIA DGX Spark (128GB) has 128 GB VRAM and 273 GB/s memory bandwidth. It can run 61 of our 80 tracked models natively in VRAM at 8k context.
With 128 GB LPDDR5X, the NVIDIA DGX Spark (128GB) is a workstation-tier GPU that can run 61 models natively. It handles 70B-class models at Q4 quantization.
The NVIDIA DGX Spark is a compact desktop AI supercomputer released in 2025 (previously announced as Project DIGITS). It pairs an ARM-based Grace CPU with a Blackwell GPU on a single chip, sharing 128GB of LPDDR5X unified memory — the same architecture as the GB10 Grace Blackwell Superchip. At 273 GB/s, bandwidth is lower than discrete HBM-based GPUs, but the 128GB capacity means you can run 70B models at Q4 and many 405B models with CPU offload — all on a device the size of a Mac mini, at a starting price under $4,000.
NVIDIA DGX Spark (128GB): 2025 desktop AI supercomputer with ARM Grace CPU + Blackwell GPU sharing 128GB LPDDR5X at 273 GB/s.
70B at Q4 native. 405B at Q2-Q3 with CPU offload. Compact form factor at ~$4,000 starting price.
llama.cpp CUDA backend works. NVIDIA NIM microservices supported. ARM architecture may need compilation.
| Vendor | NVIDIA |
| Architecture | Grace Blackwell |
| VRAM | 128 GB (unified) |
| Memory type | LPDDR5X |
| Memory bandwidth | 273 GB/s |
| Compute backend | CUDA |
| Tier | Workstation |
| Released | 2025 |
| Models (native) | 61 / 80 |
| Models (offload) | 0 / 80 |
Software: ARM-based Grace CPU + Blackwell GPU share unified memory. llama.cpp CUDA backend works; NVIDIA NIM microservices also supported.
Popular models for this GPU
Models this GPU runs natively in VRAM (61)
- Qwen3 235B-A22B (MoE)235B · MMLU-Pro 84.4Q2_K · ~6 t/s
- MiniMax M2.5 229B229B · MMLU-Pro 84.8Q2_K · ~12 t/s
- MiniMax M2.7 229B229B · MMLU-Pro 86.0Q2_K · ~12 t/s
- Step 3.7 Flash198B · MMLU-Pro —NVFP4 · ~8.9 t/s
- Step 3.5 Flash196.81B · MMLU-Pro 84.4NVFP4 · ~8.9 t/s
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0NVFP4 · ~2.7 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7NVFP4 · ~9.4 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7NVFP4 · ~8.6 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7NVFP4 · ~19.9 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3NVFP4 · ~5.7 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4NVFP4 · ~8.2 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9NVFP4 · ~8.2 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1NVFP4 · ~4.6 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9NVFP4 · ~4.7 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0NVFP4 · ~4.7 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4NVFP4 · ~4.7 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7BF16 · ~2 t/s
- Command-R 35B35B · MMLU-Pro 33.0BF16 · ~2.2 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 84.2BF16 · ~8.5 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2BF16 · ~2.5 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0BF16 · ~2.5 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5BF16 · ~2.7 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0BF16 · ~2.6 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 50.4BF16 · ~2.6 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0BF16 · ~2.6 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3BF16 · ~8.7 t/s
- Gemma 4 31B31B · MMLU-Pro 85.2BF16 · ~2.7 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5BF16 · ~8.5 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0BF16 · ~3.1 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5BF16 · ~3.2 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2BF16 · ~3.2 t/s
- Bonsai 27B27B · MMLU-Pro 81.5Ternary (Q2_0) · ~20.1 t/s
- Gemma 4 26B (MoE)26B · MMLU-Pro 82.6FP32 · ~3.4 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8FP32 · ~1.8 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2FP32 · ~2 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9FP32 · ~3.3 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0FP32 · ~2.9 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7FP32 · ~2.9 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4FP32 · ~3.1 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6FP32 · ~3.5 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6FP32 · ~3.6 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2FP32 · ~3.5 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0FP32 · ~4.5 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3FP32 · ~5.4 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0FP32 · ~5.4 t/s
- Qwen3 8B8B · MMLU-Pro 56.7FP32 · ~5.3 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3FP32 · ~5.7 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0FP32 · ~5.9 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6FP32 · ~10.8 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4FP32 · ~10.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4FP32 · ~9.6 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0FP32 · ~12.9 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4FP32 · ~14 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8FP32 · ~15.7 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0FP32 · ~21.1 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0FP32 · ~21.1 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8FP32 · ~28.5 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5FP32 · ~33.9 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7FP32 · ~41 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0FP32 · ~84.5 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0FP32 · ~99.9 t/s
Too large for this GPU (19)
Compare NVIDIA DGX Spark (128GB) with other GPUs
Frequently asked questions
- How much VRAM does the NVIDIA DGX Spark (128GB) have?
- The NVIDIA DGX Spark (128GB) has 128 GB of LPDDR5X with 273 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the NVIDIA DGX Spark (128GB) best for?
- With 128 GB of VRAM, the NVIDIA DGX Spark (128GB) is a server-class GPU designed for running the largest open-weight models (70B–405B) at high quantization with ample context.
- What LLMs can the NVIDIA DGX Spark (128GB) run locally?
- The NVIDIA DGX Spark (128GB) can run 61 of the 80 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.3 70B Instruct at NVFP4, Llama 3.1 8B Instruct at FP32, Llama 3.2 3B Instruct at FP32.
- Can the NVIDIA DGX Spark (128GB) run Llama 3.3 70B Instruct?
- Yes. The NVIDIA DGX Spark (128GB) runs Llama 3.3 70B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 4.7 tokens per second.
- Can the NVIDIA DGX Spark (128GB) run Qwen 3.6 27B?
- Yes. The NVIDIA DGX Spark (128GB) runs Qwen 3.6 27B natively in VRAM at BF16 quantization, achieving approximately 3.2 tokens per second.
- Can the NVIDIA DGX Spark (128GB) run Llama 3.1 8B Instruct?
- Yes. The NVIDIA DGX Spark (128GB) runs Llama 3.1 8B Instruct natively in VRAM at FP32 quantization, achieving approximately 5.4 tokens per second.