NousResearch Hermes 4 14B needs ~13.7 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~8 tok/s.
Operating mode
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
Fit status
Runs well
Decode
7.6 tok/s
TTFT
25436 ms
Safe context
51K
Memory
13.7 GB / 17.3 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.6 tok/s | 13874 ms | 51K |
| Coding | C | Runs well | 7.6 tok/s | 25436 ms | 51K |
| Agentic Coding | C | Tight fit | 7.6 tok/s | 36998 ms | 51K |
| Reasoning | C | Runs well | 7.6 tok/s | 30061 ms | 51K |
| RAG | C | Tight fit | 7.6 tok/s | 46247 ms | 51K |
How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C48 |
Q3_K_S | 3 | 6.9 GB | Low | C50 |
NVFP4 | 4 |
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 options
Raises estimated decode speed by about 258%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, Mac mini M2 24GB can run NousResearch Hermes 4 14B with a C grade (Runs well). Expected decode speed: 7.6 tok/s.
NousResearch Hermes 4 14B (14B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.
The recommended quantization for NousResearch Hermes 4 14B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M2 24GB, NousResearch Hermes 4 14B achieves approximately 7.6 tokens per second decode speed with a time-to-first-token of 25436ms using Q4_K_M quantization.
For coding workloads, NousResearch Hermes 4 14B on Mac mini M2 24GB receives a C grade with 7.6 tok/s and 51K context.
On Mac mini M2 24GB, NousResearch Hermes 4 14B can safely use up to 51K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--nousresearch-hermes-4-14b-gguf-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.8 GB |
| Medium |
| C51 |
Q4_K_M | 4 | 8.5 GB | Medium | C51 |
Q5_K_M | 5 | 10.1 GB | High | C51 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | C51 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. Mac mini M2 24GB 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.