Hermes 2 Pro Llama 3 8B needs ~11.9 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~43 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
39.6 tok/s
TTFT
4885 ms
Safe context
403K
Memory
11.9 GB / 34.6 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 | 43.1 tok/s | 2451 ms | 403K |
| Coding | C | Runs well | 43.1 tok/s | 4494 ms | 403K |
| Agentic Coding | C | Runs well | 43.1 tok/s | 6537 ms | 403K |
| Reasoning | C | Runs well | 43.1 tok/s | 5312 ms | 403K |
| RAG | C | Runs well | 43.1 tok/s | 8172 ms | 403K |
How Hermes 2 Pro Llama 3 8B (8B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C43 |
Q3_K_S | 3 | 3.9 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run Hermes 2 Pro Llama 3 8B on your machine.
Run
lms load hf-nousresearch--hermes-2-pro-llama-3-8b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 140%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 48GB can run Hermes 2 Pro Llama 3 8B with a C grade (Runs well). Expected decode speed: 43.1 tok/s.
Hermes 2 Pro Llama 3 8B (8B parameters) requires approximately 11.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Hermes 2 Pro Llama 3 8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 48GB, Hermes 2 Pro Llama 3 8B achieves approximately 43.1 tokens per second decode speed with a time-to-first-token of 4494ms using Q4_K_M quantization.
For coding workloads, Hermes 2 Pro Llama 3 8B on MacBook Pro M4 Pro 48GB receives a C grade with 43.1 tok/s and 403K context.
On MacBook Pro M4 Pro 48GB, Hermes 2 Pro Llama 3 8B can safely use up to 403K 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-llama-3-8b-gguf-on-m4-pro-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 4.9 GB | Medium | C43 |
Q5_K_M | 5 | 5.8 GB | High | C43 |
Q6_K | 6 | 6.6 GB | High | C44 |
Q8_0 | 8 | 8.6 GB | Very High | C44 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C48 |
Not always. MacBook Pro M4 Pro 48GB 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.