NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 921%.196 tok/sデコード
Raises estimated decode speed by about 921%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can NousResearch Hermes 4 14B run on NVIDIA DGX Spark 128GB?
YES — With F16
C43Usable
Estimated from fit model
NousResearch Hermes 4 14B needs ~44.6 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~8 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth
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.
NousResearch Hermes 4 14B at Q4_K_M needs 11.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (44.6 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 24.4 GB, 19.2 tok/s, Runs well
24.4 GB required108.8 GB available
22% VRAM used
Fit status
Runs well
Decode
19.2 tok/s
TTFT
10094 ms
Safe context
839K
Memory
24.4 GB / 108.8 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsNousResearch Hermes 4 14B 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: 19.2 tok/s decode · 10.1s TTFT (warm) · 48 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 | 19.2 tok/s | 5506 ms | 839K |
| Coding | F | Too heavy | 3.5 tok/s | 56076 ms | 4K |
| Agentic Coding | C | Runs well | 19.2 tok/s | 14682 ms | 839K |
| Reasoning | C | Runs well | 19.2 tok/s | 11929 ms | 839K |
| RAG | C | Runs well | 19.2 tok/s | 18352 ms | 839K |
Quantization options
How NousResearch Hermes 4 14B (14B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | D39 |
Q3_K_S | 3 | 6.9 GB | Low | D39 |
NVFP4 | 4 | 7.8 GB | Medium | D39 |
Q4_K_M | 4 | 8.5 GB | Medium | D39 |
Q5_K_M | 5 | 10.1 GB | High | D39 |
Q6_K | 6 | 11.5 GB | High | D40 |
Q8_0 | 8 | 15.0 GB | Very High | D40 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C42 |
Get started
Copy-paste commands to run NousResearch Hermes 4 14B on your machine.
Run
lms load hf-bartowski--nousresearch-hermes-4-14b-gguf && lms server startアップグレードオプション
NousResearch Hermes 4 14B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 921%.196 tok/sデコード
Raises estimated decode speed by about 921%.
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 921%.196 tok/sデコード
Raises estimated decode speed by about 921%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run NousResearch Hermes 4 14B?
Yes, NVIDIA DGX Spark 128GB can run NousResearch Hermes 4 14B at F16 quantization (Runs well). The recommended Q4_K_M requires 11.4 GB which exceeds available memory, but at F16 it needs only 44.6 GB. Expected decode speed: 8.0 tok/s.
How much VRAM does NousResearch Hermes 4 14B need?
NousResearch Hermes 4 14B (14B parameters) requires approximately 11.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 44.6 GB.
What is the best quantization for NousResearch Hermes 4 14B?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 44.6 GB.
What speed will NousResearch Hermes 4 14B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, NousResearch Hermes 4 14B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24230ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run NousResearch Hermes 4 14B for coding?
For coding workloads, NousResearch Hermes 4 14B on NVIDIA DGX Spark 128GB receives a F grade with 3.5 tok/s and 4K context.
What context window can NousResearch Hermes 4 14B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, NousResearch Hermes 4 14B can safely use up to 642K tokens of context at F16 quantization. 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 NousResearch Hermes 4 14B?
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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