Raises estimated decode speed by about 113%.
~$3,999 MSRP
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VOOZH | about |
Hermes 4.3 36B needs ~34.0 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~11 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
10.9 tok/s
TTFT
17714 ms
Safe context
62K
Memory
34.0 GB / 46.1 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 | 10.9 tok/s | 9662 ms | 62K |
| Coding | C | Runs well | 10.9 tok/s | 17714 ms | 62K |
| Agentic Coding | C | Tight fit | 10.9 tok/s | 25766 ms | 62K |
| Reasoning | C | Runs well | 10.9 tok/s | 20935 ms | 62K |
| RAG | C | Tight fit | 10.9 tok/s | 32208 ms | 62K |
How Hermes 4.3 36B (36B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 14.0 GB | Low | C45 |
Q3_K_S | 3 | 17.6 GB | Low | C46 |
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
Raises estimated decode speed by about 113%.
~$3,999 MSRP
Raises estimated decode speed by about 113%.
~$3,999 MSRP
20.2 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 22.0 GB | Medium | C47 |
Q5_K_M | 5 | 25.9 GB | High | C48 |
Q6_KBest for your GPU | 6 | 29.5 GB | High | C48 |
Q8_0 | 8 | 38.5 GB | Very High | F0 |
F16 | 16 | 73.8 GB | Maximum | F0 |
Not always. MacBook Pro M3 Max 64GB 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.