Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Qwen 2.5 Coder 1.5B needs ~17.8 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~21 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
21.0 tok/s
TTFT
9219 ms
Safe context
33K
Memory
15.6 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 | 21.0 tok/s | 5029 ms | 4K |
| Coding | F | Too heavy | 21.0 tok/s | 9219 ms | 4K |
| Agentic Coding | F | Too heavy | 21.0 tok/s | 13410 ms | 4K |
| Reasoning | F | Too heavy | 21.0 tok/s | 10895 ms | 4K |
| RAG | F | Too heavy | 21.0 tok/s | 16762 ms | 4K |
How Qwen 2.5 Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | B58 |
Q3_K_S | 3 | 0.7 GB | Low | B58 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 2.5 Coder 1.5B on your machine.
Run
ollama run qwen2.5-coder:1.5bUpgrade options
Yes, NVIDIA DGX Spark 128GB can run Qwen 2.5 Coder 1.5B at F16 quantization (Runs well). The recommended Q4_K_M requires 2.5 GB which exceeds available memory, but at F16 it needs only 17.8 GB. Expected decode speed: 21.0 tok/s.
Qwen 2.5 Coder 1.5B (1.5B parameters) requires approximately 2.5 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 17.8 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 17.8 GB.
On NVIDIA DGX Spark 128GB, Qwen 2.5 Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using F16 quantization.
For coding workloads, Qwen 2.5 Coder 1.5B on NVIDIA DGX Spark 128GB receives a F grade with 21.0 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Qwen 2.5 Coder 1.5B can safely use up to 33K tokens of context at F16 quantization. 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-coder-1.5b-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:
0.8 GB |
| Medium |
| B58 |
Q4_K_M | 4 | 0.9 GB | Medium | B58 |
Q5_K_M | 5 | 1.1 GB | High | B58 |
Q6_K | 6 | 1.2 GB | High | B58 |
Q8_0 | 8 | 1.6 GB | Very High | B58 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | B58 |
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.