Can I Run Qwen 2.5 72B Instruct on NVIDIA DGX Spark (128GB Grace-Blackwell Unified)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 2.5 72B Instruct (72.7B) on NVIDIA DGX Spark (128GB Grace-Blackwell Unified) (118 GB).
✓ Fits Comfortably in VRAM
Local Hardware Configurations & Pricing
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context
NVIDIA DGX Spark (128GB Grace-Blackwell Unified)
128GB Unified Memory with native CUDA + Blackwell FP4 support
Cloud Instance Options for Qwen 2.5 72B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 145.4 GB | 5.37 GB | 156.61 GB (133%) | ~1 t/s | PARTIAL |
| Q8_08.5 bpw | 77.06 GB | 5.37 GB | 85.88 GB (73%) | ~2 t/s | FITS |
| Q6_K6.56 bpw | 59.61 GB | 5.37 GB | 67.82 GB (57%) | ~3 t/s | FITS |
| Q5_K_M5.68 bpw | 51.62 GB | 5.37 GB | 59.55 GB (50%) | ~3 t/s | FITS |
| Q4_K_M4.8 bpw | 43.62 GB | 5.37 GB | 51.27 GB (43%) | ~4 t/s | FITS |
| FP44.25 bpw | 38.53 GB | 5.37 GB | 46 GB (39%) | ~5 t/s | FITS |
| IQ3_XS3.3 bpw | 29.81 GB | 5.37 GB | 36.97 GB (31%) | ~6 t/s | FITS |
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Frequently Asked Questions: Qwen 2.5 72B Instruct on NVIDIA DGX Spark (128GB Grace-Blackwell Unified)
How much VRAM does Qwen 2.5 72B Instruct need on NVIDIA DGX Spark (128GB Grace-Blackwell Unified)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 2.5 72B Instruct requires 51.27 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Qwen 2.5 72B Instruct achieve on NVIDIA DGX Spark (128GB Grace-Blackwell Unified)?
Based on NVIDIA DGX Spark (128GB Grace-Blackwell Unified)'s 273 GB/s memory bandwidth, expected decode speed is approximately 4 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.