NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 138%.98 tok/sデコード
Raises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can OpenHermes 2.5 7B run on NVIDIA DGX Spark 128GB?
YES — With F16
C43Usable
Estimated from fit model
OpenHermes 2.5 7B needs ~30.6 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~17 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.
OpenHermes 2.5 7B at Q4_K_M needs 7.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (30.6 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 20.5 GB, 41.2 tok/s, Runs well
20.5 GB required108.8 GB available
19% VRAM used
Fit status
Runs well
Decode
41.2 tok/s
TTFT
4695 ms
Safe context
8K
Memory
20.5 GB / 108.8 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsOpenHermes 2.5 7B 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: 41.2 tok/s decode · 4.7s TTFT (warm) · 103 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 | C | Runs well | 41.2 tok/s | 2561 ms | 8K |
| Coding | F | Too heavy | 6.9 tok/s | 28038 ms | 4K |
| Agentic Coding | C | Runs well | 41.2 tok/s | 6829 ms | 8K |
| Reasoning | C | Runs well | 41.2 tok/s | 5548 ms | 8K |
| RAG | C | Runs well | 41.2 tok/s | 8536 ms | 8K |
Quantization options
How OpenHermes 2.5 7B (7B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | D40 |
Q3_K_S | 3 | 3.4 GB | Low | D40 |
NVFP4 | 4 | 3.9 GB | Medium | D40 |
Q4_K_M | 4 | 4.3 GB | Medium | D40 |
Q5_K_M | 5 | 5.0 GB | High | C40 |
Q6_K | 6 | 5.7 GB | High | C40 |
Q8_0 | 8 | 7.5 GB | Very High | C40 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C41 |
Get started
Copy-paste commands to run OpenHermes 2.5 7B on your machine.
Run
ollama run openhermesアップグレードオプション
OpenHermes 2.5 7B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 138%.98 tok/sデコード
Raises estimated decode speed by about 138%.
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 138%.98 tok/sデコード
Raises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run OpenHermes 2.5 7B?
Yes, NVIDIA DGX Spark 128GB can run OpenHermes 2.5 7B at F16 quantization (Runs well). The recommended Q4_K_M requires 7.4 GB which exceeds available memory, but at F16 it needs only 30.6 GB. Expected decode speed: 17.2 tok/s.
How much VRAM does OpenHermes 2.5 7B need?
OpenHermes 2.5 7B (7B parameters) requires approximately 7.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 30.6 GB.
What is the best quantization for OpenHermes 2.5 7B?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 30.6 GB.
What speed will OpenHermes 2.5 7B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, OpenHermes 2.5 7B achieves approximately 17.2 tokens per second decode speed with a time-to-first-token of 11270ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run OpenHermes 2.5 7B for coding?
For coding workloads, OpenHermes 2.5 7B on NVIDIA DGX Spark 128GB receives a F grade with 6.9 tok/s and 4K context.
What context window can OpenHermes 2.5 7B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, OpenHermes 2.5 7B can safely use up to 8K tokens of context at F16 quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for OpenHermes 2.5 7B?
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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