MacBook Pro M4 Pro 64GBBudget pick
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.17.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Hermes 4.3 36B needs ~32.3 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~16 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
Tight fit
Decode
27.3 tok/s
TTFT
7079 ms
Safe context
25K
Memory
32.3 GB / 34.6 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 | C | Tight fit | 27.3 tok/s | 3861 ms | 25K |
| Coding | C | Tight fit | 15.7 tok/s | 12360 ms | 25K |
| Agentic Coding | D | Runs with offload (needs ~1.2 GB host RAM) | 24.8 tok/s | 11376 ms | 25K |
| Reasoning | C | Tight fit | 27.3 tok/s | 8366 ms | 25K |
| RAG | D | Runs with offload (needs ~1.2 GB host RAM) | 24.8 tok/s | 14220 ms |
How Hermes 4.3 36B (36B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 14.0 GB | Low | C47 |
Q3_K_S | 3 | 17.6 GB | Low | C49 |
NVFP4 | 4 |
Copy-paste commands to run Hermes 4.3 36B on your machine.
Run
lms load hf-nousresearch--hermes-4-3-36b-gguf && lms server startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Max 48GB can run Hermes 4.3 36B with a C grade (Tight fit). Expected decode speed: 15.7 tok/s.
Hermes 4.3 36B (36B parameters) requires approximately 32.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Hermes 4.3 36B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 48GB, Hermes 4.3 36B achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12360ms using Q4_K_M quantization.
For coding workloads, Hermes 4.3 36B on MacBook Pro M4 Max 48GB receives a C grade with 15.7 tok/s and 25K context.
On MacBook Pro M4 Max 48GB, Hermes 4.3 36B can safely use up to 25K 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-4-3-36b-gguf-on-m4-max-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 25K |
20.2 GB |
| Medium |
| C49 |
Q4_K_M | 4 | 22.0 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 25.9 GB | High | C48 |
Q6_K | 6 | 29.5 GB | High | F0 |
Q8_0 | 8 | 38.5 GB | Very High | F0 |
F16 | 16 | 73.8 GB | Maximum | F0 |
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 M4 Max 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.