Yi 1.5 9B needs ~34.2 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~14 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
32.4 tok/s
TTFT
5967 ms
Safe context
4K
Memory
21.2 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 | 5.4 tok/s | 19663 ms | 4K |
| Coding | F | Too heavy | 5.4 tok/s | 36049 ms | 4K |
| Agentic Coding | F | Too heavy | 5.4 tok/s | 52435 ms | 4K |
| Reasoning | F | Too heavy | 5.4 tok/s | 42603 ms | 4K |
| RAG | F | Too heavy | 5.4 tok/s | 65543 ms | 4K |
How Yi 1.5 9B (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C44 |
Q3_K_S | 3 | 4.4 GB | Low | C44 |
NVFP4 | 4 | 5.0 GB | Medium | C44 |
Q4_K_M | 4 | 5.5 GB | Medium | C44 |
Q5_K_M | 5 | 6.5 GB | High | C44 |
Q6_K | 6 | 7.4 GB | High | C44 |
Q8_0 | 8 | 9.6 GB | Very High | C44 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C45 |
Copy-paste commands to run Yi 1.5 9B on your machine.
Run
lms load Yi-1.5-9B-Chat && lms server startOpciones de mejora
NVIDIA H200 141GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 289%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 289%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA B200 180GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 289%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Yi 1.5 9B at F16 quantization (Runs well). The recommended Q4_K_M requires 8.2 GB which exceeds available memory, but at F16 it needs only 34.2 GB. Expected decode speed: 13.5 tok/s.
Yi 1.5 9B (9B parameters) requires approximately 8.2 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 34.2 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 34.2 GB.
On NVIDIA DGX Spark 128GB, Yi 1.5 9B achieves approximately 13.5 tokens per second decode speed with a time-to-first-token of 14323ms using F16 quantization.
For coding workloads, Yi 1.5 9B on NVIDIA DGX Spark 128GB receives a F grade with 5.4 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Yi 1.5 9B can safely use up to 4K tokens of context at F16 quantization. The model's official context limit is 4K, 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/yi-1.5-9b-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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