Hermes 2 Pro Mistral 7B needs ~16.4 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~81 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
87.8 tok/s
TTFT
2205 ms
Safe context
1.0M
Memory
16.4 GB / 69.1 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 | 87.8 tok/s | 1203 ms | 1.0M |
| Coding | C | Runs well | 80.6 tok/s | 2403 ms | 1.0M |
| Agentic Coding | C | Runs well | 87.8 tok/s | 3207 ms | 1.0M |
| Reasoning | C | Runs well | 87.8 tok/s | 2606 ms | 1.0M |
| RAG | C | Runs well | 87.8 tok/s | 4009 ms | 1.0M |
How Hermes 2 Pro Mistral 7B (7B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C40 |
Q3_K_S | 3 | 3.4 GB | Low | C40 |
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 96GB can run Hermes 2 Pro Mistral 7B with a C grade (Runs well). Expected decode speed: 80.6 tok/s.
Hermes 2 Pro Mistral 7B (7B parameters) requires approximately 16.4 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 96GB, Hermes 2 Pro Mistral 7B achieves approximately 80.6 tokens per second decode speed with a time-to-first-token of 2403ms using Q4_K_M quantization.
For coding workloads, Hermes 2 Pro Mistral 7B on MacBook Pro M4 Max 96GB receives a C grade with 80.6 tok/s and 1.0M context.
On MacBook Pro M4 Max 96GB, Hermes 2 Pro Mistral 7B can safely use up to 1.0M 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-96gb" 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 |
| C40 |
Q4_K_M | 4 | 4.3 GB | Medium | C40 |
Q5_K_M | 5 | 5.0 GB | High | C40 |
Q6_K | 6 | 5.7 GB | High | C40 |
Q8_0 | 8 | 7.5 GB | Very High | C40 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C42 |
Not always. MacBook Pro M4 Max 96GB 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.