README.md

metadata

license: mit
task_categories:
  - text-generation
tags:
  - benchmark
  - nvidia
  - dgx-spark
  - blackwell
  - llm
  - inference
pretty_name: DGX Spark LLM Benchmarks
size_categories:
  - n<1K

DGX Spark LLM Benchmarks

First comprehensive benchmark suite for NVIDIA DGX Spark (GB10 Blackwell).

Hardware

• GPU: NVIDIA GB10 Blackwell (1 PFLOP FP4)
• Memory: 128GB unified LPDDR5x (273 GB/s)
• CPU: 20-core ARM (10x Cortex-X925 + 10x Cortex-A725)
• Storage: 4TB NVMe
• Framework: Ollama 0.18.3
• CUDA: 13.0 | Driver: 580.142

Benchmark Results

Run 1 — General Inference (11 models)

Model	Size	Prompt tok/s	Gen tok/s	Load Time
Llama 3.1 8B	4.9 GB	574.79	42.86	4.73s
Gemma3 27B	17 GB	164.64	11.71	9.83s
Qwen2.5-Coder 32B	19 GB	288.96	10.36	15.13s
Qwen3 32B	20 GB	141.91	9.88	4.08s
CodeLlama 70B	38 GB	133.35	5.73	28.81s
Nemotron 70B	42 GB	87.97	4.77	27.25s
Llama 3.1 70B	42 GB	67.92	4.76	28.35s
DeepSeek-R1 70B	42 GB	24.18	4.68	47.02s
Llama 3.3 70B	42 GB	67.97	4.66	27.41s
Qwen 2.5 72B	47 GB	122.19	4.40	44.75s
Mistral Large 123B	73 GB	10.43	2.28	86.14s

Run 2 — Coding Benchmark

Model	Gen tok/s	Tokens Generated
Llama 3.1 8B	42.51	369
Qwen2.5-Coder 32B	10.33	552
Qwen3 32B	9.38	8,180
CodeLlama 70B	5.69	752
Gemma3 27B	11.61	329
DeepSeek-R1 70B	4.51	10,710
Llama 3.3 70B	4.67	507

Run 3 — Context Scaling

Model	Short Prompt tok/s	Long Prompt tok/s	Scale	Gen tok/s
Llama 3.1 8B	574	2,090	3.6x	41.82
Gemma3 27B	164	616	3.8x	11.70
Qwen3 32B	141	491	3.5x	10.10
Llama 3.1 70B	67	212	3.2x	4.69
Qwen 2.5 72B	122	225	1.8x	4.33
Nemotron 70B	87	164	1.9x	4.62

Run 4 — Vision

Model	Size	Prompt tok/s	Gen tok/s	Load Time
Llama3.2-Vision 90B	54 GB	6.02	3.47	16.86s

Key Findings

1. 27-32B is the sweet spot — 10-12 tok/s, genuinely interactive
2. Prompt eval scales 3-4x with longer prompts on unified memory
3. DeepSeek-R1 generates 10,710 tokens of reasoning for one coding question
4. 90B vision model runs on a desktop at 3.47 tok/s
5. 123B is the ceiling — Mistral Large at 2.28 tok/s barely interactive
6. Generation speed is constant regardless of prompt length

Author

Gopi Trinadh Maddikunta

• University of Houston · MS Engineering Data Science
• Research Assistant, Dr. Peizhu Qian
• GSoC 2025 Contributor (Scala Center)
• GitHub: GOPITRINADH3561
• Website: gopitrinadh.site