Qwen 2.5 14B needs ~45.9 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~9 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
20.7 tok/s
TTFT
9346 ms
Safe context
131K
Memory
25.7 GB / 108.8 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.5 tok/s | 30587 ms | 4K |
| Coding | F | Too heavy | 3.5 tok/s | 56076 ms | 4K |
| Agentic Coding | F | Too heavy | 3.5 tok/s | 81565 ms | 4K |
| Reasoning | F | Too heavy | 3.5 tok/s | 66272 ms | 4K |
| RAG | F | Too heavy | 3.5 tok/s | 101956 ms | 4K |
How Qwen 2.5 14B (14B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | A70 |
Q3_K_S | 3 | 6.9 GB | Low | A70 |
NVFP4 | 4 | 7.8 GB | Medium | A70 |
Q4_K_M | 4 | 8.5 GB | Medium | A70 |
Q5_K_M | 5 | 10.1 GB | High | A70 |
Q6_K | 6 | 11.5 GB | High | A71 |
Q8_0 | 8 | 15.0 GB | Very High | A71 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | A73 |
Copy-paste commands to run Qwen 2.5 14B on your machine.
Run
ollama run qwen2.5Opções de upgrade
NVIDIA H200 141GBOpção econômicaRaises estimated decode speed by about 847%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMelhor custo-benefícioRaises estimated decode speed by about 847%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBUpgrade NVIDIARaises estimated decode speed by about 847%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Qwen 2.5 14B at F16 quantization (Runs well). The recommended Q4_K_M requires 12.7 GB which exceeds available memory, but at F16 it needs only 45.9 GB. Expected decode speed: 8.6 tok/s.
Qwen 2.5 14B (14B parameters) requires approximately 12.7 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 45.9 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 45.9 GB.
On NVIDIA DGX Spark 128GB, Qwen 2.5 14B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22435ms using F16 quantization.
For coding workloads, Qwen 2.5 14B on NVIDIA DGX Spark 128GB receives a F grade with 3.5 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Qwen 2.5 14B can safely use up to 131K tokens of context at F16 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-2.5-14b-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>
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