Hermes 2 Pro Mistral 7B needs ~9.9 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~66 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
65.9 tok/s
TTFT
2936 ms
Safe context
329K
Memory
9.9 GB / 25.9 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 65.9 tok/s | 1602 ms | 329K |
| Coding | C | Runs well | 65.9 tok/s | 2936 ms | 329K |
| Agentic Coding | C | Runs well | 65.9 tok/s | 4271 ms | 329K |
| Reasoning | C | Runs well | 65.9 tok/s | 3470 ms | 329K |
| RAG | C | Runs well | 65.9 tok/s | 5339 ms | 329K |
How Hermes 2 Pro Mistral 7B (7B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 |
Copy-paste commands to run Hermes 2 Pro Mistral 7B on your machine.
Run
lms load hf-nousresearch--hermes-2-pro-mistral-7b-gguf && lms server startYes, MacBook Pro M4 Max 36GB can run Hermes 2 Pro Mistral 7B with a C grade (Runs well). Expected decode speed: 65.9 tok/s.
Hermes 2 Pro Mistral 7B (7B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Hermes 2 Pro Mistral 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 36GB, Hermes 2 Pro Mistral 7B achieves approximately 65.9 tokens per second decode speed with a time-to-first-token of 2936ms using Q4_K_M quantization.
For coding workloads, Hermes 2 Pro Mistral 7B on MacBook Pro M4 Max 36GB receives a C grade with 65.9 tok/s and 329K context.
On MacBook Pro M4 Max 36GB, Hermes 2 Pro Mistral 7B can safely use up to 329K 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-nousresearch--hermes-2-pro-mistral-7b-gguf-on-m4-max-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C45 |
Q8_0 | 8 | 7.5 GB | Very High | C46 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
Not always. MacBook Pro M4 Max 36GB 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.