RTX 5060 Ti 16GB经济之选16 GB VRAM (+4)448 GB/s (+16)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can NousResearch Hermes 4 14B run on RTX 4080 Laptop 12GB?
YES — With Offload
C50Usable
Estimated from fit model
NousResearch Hermes 4 14B needs ~12.3 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 12.3 GB, 29.6 tok/s, Runs with offload (needs ~0.2 GB host RAM)
12.3 GB required12.0 GB available
103% VRAM needed
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
29.6 tok/s
TTFT
6541 ms
Safe context
13K
Memory
12.3 GB / 12.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsNousResearch Hermes 4 14B on RTX 4080 Laptop 12GB
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: 29.6 tok/s decode · 6.5s TTFT (warm) · 74 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 41.4 tok/s | 2549 ms | 13K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 29.6 tok/s | 6541 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 22.7 tok/s | 12389 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 29.6 tok/s | 7731 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 22.7 tok/s | 15486 ms | 13K |
Quantization options
How NousResearch Hermes 4 14B (14B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C52 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
NVFP4 | 4 | 7.8 GB | Medium | C52 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | C51 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
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升级选项
能流畅运行 NousResearch Hermes 4 14B 的硬件
RTX 5060 Ti 16GB经济之选RTX 4060 Ti 16GB最佳性价比16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.8 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 5070 Ti 16GBNVIDIA 升级16 GB VRAM (+4)896 GB/s (+464)
B
Raises estimated decode speed by about 120%.65.1 tok/s 解码
Raises estimated decode speed by about 120%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
Frequently asked questions
Can RTX 4080 Laptop 12GB run NousResearch Hermes 4 14B?
Yes, RTX 4080 Laptop 12GB can run NousResearch Hermes 4 14B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 29.6 tok/s.
How much VRAM does NousResearch Hermes 4 14B need?
NousResearch Hermes 4 14B (14B parameters) requires approximately 12.3 GB of memory with Q4_K_M quantization.
What is the best quantization for NousResearch Hermes 4 14B?
The recommended quantization for NousResearch Hermes 4 14B is Q4_K_M, which balances quality and memory efficiency.
What speed will NousResearch Hermes 4 14B run at on RTX 4080 Laptop 12GB?
On RTX 4080 Laptop 12GB, NousResearch Hermes 4 14B achieves approximately 29.6 tokens per second decode speed with a time-to-first-token of 6541ms using Q4_K_M quantization.
Can RTX 4080 Laptop 12GB run NousResearch Hermes 4 14B for coding?
For coding workloads, NousResearch Hermes 4 14B on RTX 4080 Laptop 12GB receives a C grade with 29.6 tok/s and 13K context.
What context window can NousResearch Hermes 4 14B use on RTX 4080 Laptop 12GB?
On RTX 4080 Laptop 12GB, NousResearch Hermes 4 14B can safely use up to 13K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if NousResearch Hermes 4 14B feels slow on RTX 4080 Laptop 12GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--nousresearch-hermes-4-14b-gguf-on-rtx-4080-laptop-12gb" 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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