Qwen 2.5 VL 72B needs ~62.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~4 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
4.1 tok/s
TTFT
47734 ms
Safe context
33K
Memory
62.8 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 4.1 tok/s | 26037 ms | 33K |
| Coding | S | Runs well | 4.1 tok/s | 47734 ms | 33K |
| Agentic Coding | S | Runs well | 4.1 tok/s | 69431 ms | 33K |
| Reasoning | S | Runs well | 4.1 tok/s | 56412 ms | 33K |
| RAG | S | Runs well | 4.1 tok/s | 86788 ms | 33K |
How Qwen 2.5 VL 72B (72B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 28.1 GB | Low | A83 |
Q3_K_S | 3 | 35.3 GB | Low | A85 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 VL 72B on your machine.
Run
lms load Qwen2.5-VL-72B-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 2.4 tok/s | |
 Qwen 3.5 122B A10B | 122B | S |
Yes, NVIDIA DGX Spark 128GB can run Qwen 2.5 VL 72B with a S grade (Runs well). Expected decode speed: 4.1 tok/s.
Qwen 2.5 VL 72B (72B parameters) requires approximately 62.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 2.5 VL 72B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Qwen 2.5 VL 72B achieves approximately 4.1 tokens per second decode speed with a time-to-first-token of 47734ms using Q4_K_M quantization.
For coding workloads, Qwen 2.5 VL 72B on NVIDIA DGX Spark 128GB receives a S grade with 4.1 tok/s and 33K context.
On NVIDIA DGX Spark 128GB, Qwen 2.5 VL 72B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-2.5-vl-72b-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
40.3 GB |
| Medium |
| S86 |
Q4_K_M | 4 | 43.9 GB | Medium | S87 |
Q5_K_M | 5 | 51.8 GB | High | S88 |
Q6_K | 6 | 59.0 GB | High | S88 |
Q8_0Best for your GPU | 8 | 77.0 GB | Very High | S88 |
F16 | 16 | 147.6 GB | Maximum | F0 |
| 6.6 tok/s |
| Mistral Small 4 119B | 119B | S | 7.1 tok/s |
 GPT-OSS 120B | 117B | A | 2.5 tok/s |
 Command A 111B | 111B | S | 2.6 tok/s |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.