HelpingAI 15B i1 needs ~46.8 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~8 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
17.9 tok/s
TTFT
10815 ms
Safe context
777K
Memory
25.2 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 | C | Runs well | 17.9 tok/s | 5899 ms | 777K |
| Coding | F | Too heavy | 3.2 tok/s | 60081 ms | 4K |
| Agentic Coding | C | Runs well | 17.9 tok/s | 15730 ms | 777K |
| Reasoning | C | Runs well | 17.9 tok/s | 12781 ms | 777K |
| RAG | C | Runs well | 17.9 tok/s | 19663 ms | 777K |
How HelpingAI 15B i1 (15B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | D39 |
Q3_K_S | 3 | 7.4 GB | Low | D39 |
NVFP4 | 4 | 8.4 GB | Medium | D39 |
Q4_K_M | 4 | 9.2 GB | Medium | D39 |
Q5_K_M | 5 | 10.8 GB | High | D39 |
Q6_K | 6 | 12.3 GB | High | D39 |
Q8_0 | 8 | 16.1 GB | Very High | D40 |
F16Best for your GPU | 16 | 30.7 GB | Maximum | C42 |
Copy-paste commands to run HelpingAI 15B i1 on your machine.
Run
lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server startOpciones de mejora
NVIDIA H200 141GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 1073%.
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 1073%.
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 1073%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run HelpingAI 15B i1 at F16 quantization (Runs well). The recommended Q4_K_M requires 12.1 GB which exceeds available memory, but at F16 it needs only 46.8 GB. Expected decode speed: 7.5 tok/s.
HelpingAI 15B i1 (15B parameters) requires approximately 12.1 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 46.8 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 46.8 GB.
On NVIDIA DGX Spark 128GB, HelpingAI 15B i1 achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25960ms using F16 quantization.
For coding workloads, HelpingAI 15B i1 on NVIDIA DGX Spark 128GB receives a F grade with 3.2 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, HelpingAI 15B i1 can safely use up to 581K tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
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.
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