Hermes 4.3 36B needs ~26.2 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q3_K_S quantization, expect ~10 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
7.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.8 tok/s
TTFT
28632 ms
Safe context
4K
Memory
30.5 GB / 23.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 7.4 tok/s | 14327 ms | 4K |
| Coding | F | Too heavy | 6.8 tok/s | 28632 ms | 4K |
| Agentic Coding | F | Too heavy | 5.8 tok/s | 48375 ms | 4K |
| Reasoning | F | Too heavy | 6.8 tok/s | 33838 ms | 4K |
| RAG | F | Too heavy | 5.8 tok/s | 60469 ms | 4K |
How Hermes 4.3 36B (36B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 14.0 GB | Low | C50 |
Q3_K_S | 3 | 17.6 GB | Low | F0 |
NVFP4 | 4 | 20.2 GB | Medium | F0 |
Q4_K_M | 4 | 22.0 GB | Medium | F0 |
Q5_K_M | 5 | 25.9 GB | High | F0 |
Q6_K | 6 | 29.5 GB | High | F0 |
Q8_0 | 8 | 38.5 GB | Very High | F0 |
F16 | 16 | 73.8 GB | Maximum | F0 |
Copy-paste commands to run Hermes 4.3 36B on your machine.
Run
lms load hf-nousresearch--hermes-4-3-36b-gguf && lms server startOpciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,099 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,599 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
~$1,999 MSRP
Yes, MacBook Pro M1 Max 32GB can run Hermes 4.3 36B at Q3_K_S quantization (Very compromised (needs ~2.1 GB host RAM)). The recommended Q4_K_M requires 30.5 GB which exceeds available memory, but at Q3_K_S it needs only 26.2 GB. Expected decode speed: 9.5 tok/s.
Hermes 4.3 36B (36B parameters) requires approximately 30.5 GB at Q4_K_M quantization. On MacBook Pro M1 Max 32GB, it fits at Q3_K_S using 26.2 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M1 Max 32GB the best fitting quantization is Q3_K_S, which uses 26.2 GB.
On MacBook Pro M1 Max 32GB, Hermes 4.3 36B achieves approximately 9.5 tokens per second decode speed with a time-to-first-token of 20474ms using Q3_K_S quantization.
For coding workloads, Hermes 4.3 36B on MacBook Pro M1 Max 32GB receives a F grade with 6.8 tok/s and 4K context.
On MacBook Pro M1 Max 32GB, Hermes 4.3 36B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M1 Max 32GB 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.
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