MacBook Pro M3 Pro 36GBコスパ重視
36 GB Unified (+4)150 GB/s (+30)
C
Raises estimated decode speed by about 44%.12.8 tok/sデコード
Raises estimated decode speed by about 44%.
〜$1,999 MSRP
Can NousResearch Hermes 4 14B run on Mac mini M4 32GB?
YES — Runs Great
C48Usable
Estimated — low-sample bucket· few comparable runs
NousResearch Hermes 4 14B needs ~14.5 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 14.5 GB, 8.9 tok/s, Runs well
14.5 GB required23.0 GB available
63% VRAM used
Fit status
Runs well
Decode
8.9 tok/s
TTFT
21745 ms
Safe context
99K
Memory
14.5 GB / 23.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsNousResearch Hermes 4 14B on Mac mini M4 32GB
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: 8.9 tok/s decode · 21.7s TTFT (warm) · 22 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 | 8.9 tok/s | 11861 ms | 99K |
| Coding | C | Runs well | 8.9 tok/s | 21745 ms | 99K |
| Agentic Coding | C | Runs well | 8.9 tok/s | 31630 ms | 99K |
| Reasoning | C | Runs well | 8.9 tok/s | 25699 ms | 99K |
| RAG | C | Runs well | 8.9 tok/s | 39537 ms | 99K |
Quantization options
How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C46 |
Q3_K_S | 3 | 6.9 GB | Low | C47 |
NVFP4 | 4 | 7.8 GB | Medium | C47 |
Q4_K_M | 4 | 8.5 GB | Medium | C48 |
Q5_K_M | 5 | 10.1 GB | High | C49 |
Q6_K | 6 | 11.5 GB | High | C50 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | C50 |
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
MacBook Pro M4 Max 36GBコスパ最良
36 GB Unified (+4)410 GB/s (+290)
C
Raises estimated decode speed by about 239%.30.2 tok/sデコード
Raises estimated decode speed by about 239%.
〜$2,499 MSRP
Mac Studio M2 Ultra 64GBAppleアップグレード
64 GB Unified (+32)800 GB/s (+680)
C
Raises estimated decode speed by about 510%.54.3 tok/sデコード
Raises estimated decode speed by about 510%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Frequently asked questions
Can Mac mini M4 32GB run NousResearch Hermes 4 14B?
Yes, Mac mini M4 32GB can run NousResearch Hermes 4 14B with a C grade (Runs well). Expected decode speed: 8.9 tok/s.
How much VRAM does NousResearch Hermes 4 14B need?
NousResearch Hermes 4 14B (14B parameters) requires approximately 14.5 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 mini M4 32GB?
On Mac mini M4 32GB, NousResearch Hermes 4 14B achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21745ms using Q4_K_M quantization.
Can Mac mini M4 32GB run NousResearch Hermes 4 14B for coding?
For coding workloads, NousResearch Hermes 4 14B on Mac mini M4 32GB receives a C grade with 8.9 tok/s and 99K context.
What context window can NousResearch Hermes 4 14B use on Mac mini M4 32GB?
On Mac mini M4 32GB, NousResearch Hermes 4 14B can safely use up to 99K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac mini M4 32GB as fast as VRAM for NousResearch Hermes 4 14B?
Not always. Mac mini M4 32GB 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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