Nous Hermes 1.0 needs ~22.1 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~42 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 with offload
Decode
42.3 tok/s
TTFT
4581 ms
Safe context
16K
Memory
22.1 GB / 23.0 GB
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 42.3 tok/s | 2499 ms | 16K |
| Coding | A | Runs with offload | 42.3 tok/s | 4581 ms | 16K |
| Agentic Coding | F | Too heavy | 25.0 tok/s | 11283 ms | 16K |
| Reasoning | A | Runs with offload | 42.3 tok/s | 5414 ms | 16K |
| RAG | F | Too heavy | 25.0 tok/s | 14104 ms | 16K |
How Nous Hermes 1.0 (9B params) fits at each quantization level on MacBook Pro M2 Max 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 |
Copy-paste commands to run Nous Hermes 1.0 on your machine.
Run
lms load Nous-Hermes-1.0 && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 31.5 tok/s | |
| Qwen 3.5 27B | 27B | S | 14.1 tok/s |
Yes, MacBook Pro M2 Max 32GB can run Nous Hermes 1.0 with a A grade (Runs with offload). Expected decode speed: 42.3 tok/s.
Nous Hermes 1.0 (9B parameters) requires approximately 22.1 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 M2 Max 32GB, Nous Hermes 1.0 achieves approximately 42.3 tokens per second decode speed with a time-to-first-token of 4581ms using Q4_K_M quantization.
For coding workloads, Nous Hermes 1.0 on MacBook Pro M2 Max 32GB receives a A grade with 42.3 tok/s and 16K context.
On MacBook Pro M2 Max 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nous-hermes-1.0-on-m2-max-32gb" 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 |
| 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 |
| Qwen 3.6 27B | 27B | S | 11.6 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 33.3 tok/s |
| Qwen 3.5 35B A3B | 35B | A | 27.5 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M2 Max 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.