Can Nous Hermes 1.0 run on MacBook Pro M1 Pro 32GB?
YES — With Offload
Nous Hermes 1.0 needs ~22.1 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
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
Fit status
Runs with offload
Decode
23.7 tok/s
TTFT
8176 ms
Safe context
16K
Memory
22.1 GB / 23.0 GB
Memory breakdown
See how fast it feels
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.
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
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 | A | Runs well | 23.7 tok/s | 4460 ms | 16K |
| Coding | A | Runs with offload | 23.7 tok/s | 8176 ms | 16K |
| Agentic Coding | F | Too heavy | 14.0 tok/s | 20136 ms | 16K |
| Reasoning | A | Runs with offload | 23.7 tok/s | 9662 ms | 16K |
| RAG | F | Too heavy | 14.0 tok/s | 25170 ms | 16K |
Quantization options
How Nous Hermes 1.0 (9B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B66 |
Q3_K_S | 3 | 4.4 GB | Low | B66 |
NVFP4 | 4 | 5.0 GB | Medium | B66 |
Q4_K_M | 4 | 5.5 GB | Medium | B67 |
Q5_K_M | 5 | 6.5 GB | High | B67 |
Q6_K | 6 | 7.4 GB | High | B68 |
Q8_0 | 8 | 9.6 GB | Very High | B69 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | A70 |
Get started
Copy-paste commands to run Nous Hermes 1.0 on your machine.
Run
lms load Nous-Hermes-1.0 && lms server startYour hardware
More models your MacBook Pro M1 Pro 32GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | A | 17.7 tok/s | ||
| 27B | S | 7.9 tok/s | ||
| 27B | S | 6.5 tok/s | ||
| 30B | S | 18.6 tok/s | ||
| 35B | A | 15.4 tok/s |
Frequently asked questions
Can MacBook Pro M1 Pro 32GB run Nous Hermes 1.0?
Yes, MacBook Pro M1 Pro 32GB can run Nous Hermes 1.0 with a A grade (Runs with offload). Expected decode speed: 23.7 tok/s.
How much VRAM does Nous Hermes 1.0 need?
Nous Hermes 1.0 (9B parameters) requires approximately 22.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Nous Hermes 1.0?
The recommended quantization for Nous Hermes 1.0 is Q4_K_M, which balances quality and memory efficiency.
What speed will Nous Hermes 1.0 run at on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, Nous Hermes 1.0 achieves approximately 23.7 tokens per second decode speed with a time-to-first-token of 8176ms using Q4_K_M quantization.
Can MacBook Pro M1 Pro 32GB run Nous Hermes 1.0 for coding?
For coding workloads, Nous Hermes 1.0 on MacBook Pro M1 Pro 32GB receives a A grade with 23.7 tok/s and 16K context.
What context window can Nous Hermes 1.0 use on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, Nous Hermes 1.0 can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
What should I upgrade first if Nous Hermes 1.0 feels slow on MacBook Pro M1 Pro 32GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for Nous Hermes 1.0?
Not always. MacBook Pro M1 Pro 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nous-hermes-1.0-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: