NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 420%.140 tok/sデコード
Raises estimated decode speed by about 420%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can HelpingAI2.5 10B i1 run on NVIDIA DGX Spark 128GB?
YES — Runs Great
C42Usable
Estimated from fit model
HelpingAI2.5 10B i1 needs ~21.5 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~27 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 21.5 GB, 26.9 tok/s, Runs well
21.5 GB required108.8 GB available
20% VRAM used
Fit status
Runs well
Decode
26.9 tok/s
TTFT
7210 ms
Safe context
1.2M
Memory
21.5 GB / 108.8 GB
Memory breakdown
Weights6.1 GB
KV Cache1.2 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsHelpingAI2.5 10B i1 on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 26.9 tok/s decode · 7.2s TTFT (warm) · 67 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.8 tok/s | 21848 ms | 4K |
| Coding | C | Runs well | 26.9 tok/s | 7210 ms | 1.2M |
| Agentic Coding | C | Runs well | 26.9 tok/s | 10487 ms | 1.2M |
| Reasoning | C | Runs well | 26.9 tok/s | 8521 ms | 1.2M |
| RAG | C | Runs well | 26.9 tok/s | 13109 ms | 1.2M |
Quantization options
How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.9 GB | Low | D39 |
Q3_K_S | 3 | 4.9 GB | Low | D39 |
NVFP4 | 4 | 5.6 GB | Medium | D39 |
Q4_K_M | 4 | 6.1 GB | Medium | D39 |
Q5_K_M | 5 | 7.2 GB | High | D39 |
Q6_K | 6 | 8.2 GB | High | D39 |
Q8_0 | 8 | 10.7 GB | Very High | D39 |
F16Best for your GPU | 16 | 20.5 GB | Maximum | C40 |
Get started
Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.
Run
lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server startアップグレードオプション
HelpingAI2.5 10B i1を快適に動かすハードウェア
NVIDIA H200 141GBコスパ重視NVIDIA H200 PCIe 141GBコスパ最良141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 420%.140 tok/sデコード
Raises estimated decode speed by about 420%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA B200 180GBNVIDIAアップグレード180 GB VRAM (+52)8000 GB/s (+7727)
C
Raises estimated decode speed by about 420%.140 tok/sデコード
Raises estimated decode speed by about 420%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run HelpingAI2.5 10B i1?
Yes, NVIDIA DGX Spark 128GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 26.9 tok/s.
How much VRAM does HelpingAI2.5 10B i1 need?
HelpingAI2.5 10B i1 (10B parameters) requires approximately 21.5 GB of memory with Q4_K_M quantization.
What is the best quantization for HelpingAI2.5 10B i1?
The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will HelpingAI2.5 10B i1 run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, HelpingAI2.5 10B i1 achieves approximately 26.9 tokens per second decode speed with a time-to-first-token of 7210ms using Q4_K_M quantization.
Can NVIDIA DGX Spark 128GB run HelpingAI2.5 10B i1 for coding?
For coding workloads, HelpingAI2.5 10B i1 on NVIDIA DGX Spark 128GB receives a C grade with 26.9 tok/s and 1.2M context.
What context window can HelpingAI2.5 10B i1 use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, HelpingAI2.5 10B i1 can safely use up to 1.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for HelpingAI2.5 10B i1?
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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