Nous Hermes 1.0 needs ~20.5 GB but MacBook Pro M3 Pro 18GB only has 13.0 GB. Try a smaller quantization or lighter model.
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
7.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
11.0 tok/s
TTFT
17664 ms
Safe context
6K
Memory
20.5 GB / 13.0 GB
Offload
40%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 20.5 GB, but this setup only exposes 13.0 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Very compromised (needs ~0.6 GB host RAM) | 16.7 tok/s | 6314 ms | 6K |
| Coding | F | Too heavy | 11.0 tok/s | 17664 ms | 6K |
| Agentic Coding | F | Too heavy | 9.0 tok/s | 31375 ms | 6K |
| Reasoning | F | Too heavy | 11.0 tok/s | 20876 ms | 6K |
| RAG | F | Too heavy | 9.0 tok/s | 39218 ms | 6K |
How Nous Hermes 1.0 (9B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B70 |
Q3_K_S | 3 | 4.4 GB | Low | A71 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
RTX 5090 32GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
No, Nous Hermes 1.0 requires more memory than MacBook Pro M3 Pro 18GB provides.
Nous Hermes 1.0 (9B parameters) requires approximately 20.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Nous Hermes 1.0 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, Nous Hermes 1.0 achieves approximately 11.0 tokens per second decode speed with a time-to-first-token of 17664ms using Q4_K_M quantization.
For coding workloads, Nous Hermes 1.0 on MacBook Pro M3 Pro 18GB receives a F grade with 11.0 tok/s and 6K context.
On MacBook Pro M3 Pro 18GB, Nous Hermes 1.0 can safely use up to 6K tokens of context. The model's official context limit is 16K, 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/nous-hermes-1.0-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| A72 |
Q4_K_M | 4 | 5.5 GB | Medium | A72 |
Q5_K_M | 5 | 6.5 GB | High | A73 |
Q6_K | 6 | 7.4 GB | High | A72 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | A72 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. MacBook Pro M3 Pro 18GB 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.
