RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl1792 GB/s (+992)
C
Raises estimated decode speed by about 242%.176.3 tok/s Dekodierung
Raises estimated decode speed by about 242%.
ca. $9,999 MSRP
Can NousResearch Hermes 4 14B run on Mac Studio M1 Ultra 128GB?
YES — Runs Great
C46Usable
Estimated from fit model
NousResearch Hermes 4 14B needs ~24.9 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 24.9 GB, 51.5 tok/s, Runs well
24.9 GB required92.2 GB available
27% VRAM used
Fit status
Runs well
Decode
51.5 tok/s
TTFT
3758 ms
Safe context
672K
Memory
24.9 GB / 92.2 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsNousResearch Hermes 4 14B on Mac Studio M1 Ultra 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: 51.5 tok/s decode · 3.8s TTFT (warm) · 129 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 | 51.5 tok/s | 2050 ms | 672K |
| Coding | C | Runs well | 51.5 tok/s | 3758 ms | 672K |
| Agentic Coding | C | Runs well | 51.5 tok/s | 5466 ms | 672K |
| Reasoning | C | Runs well | 51.5 tok/s | 4441 ms | 672K |
| RAG | C | Runs well | 51.5 tok/s | 6832 ms | 672K |
Inference speed
NousResearch Hermes 4 14B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for NousResearch Hermes 4 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 140.6 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 89.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 80.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 76.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 75.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 65.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 54.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 35.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 35.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 33.2 | Offloads |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 19.5 | Offloads |
| RTX 4060 8GB | 8 GB | Q4_K_M | 7.2 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Mac Studio M1 Ultra 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 startUpgrade-Optionen
Hardware, die NousResearch Hermes 4 14B gut ausführt
Frequently asked questions
Can Mac Studio M1 Ultra 128GB run NousResearch Hermes 4 14B?
Yes, Mac Studio M1 Ultra 128GB can run NousResearch Hermes 4 14B with a C grade (Runs well). Expected decode speed: 51.5 tok/s.
How much VRAM does NousResearch Hermes 4 14B need?
NousResearch Hermes 4 14B (14B parameters) requires approximately 24.9 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 Mac Studio M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, NousResearch Hermes 4 14B achieves approximately 51.5 tokens per second decode speed with a time-to-first-token of 3758ms using Q4_K_M quantization.
Can Mac Studio M1 Ultra 128GB run NousResearch Hermes 4 14B for coding?
For coding workloads, NousResearch Hermes 4 14B on Mac Studio M1 Ultra 128GB receives a C grade with 51.5 tok/s and 672K context.
What context window can NousResearch Hermes 4 14B use on Mac Studio M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, NousResearch Hermes 4 14B can safely use up to 672K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for NousResearch Hermes 4 14B?
Not always. Mac Studio M1 Ultra 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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