MacBook Pro M4 Pro 64GBGünstige Wahl
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.17.2 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,599 MSRP
Can Hermes 4.3 36B run on MacBook Pro M4 Max 48GB?
YES — Tight Fit
C50Usable
Estimated from fit model
Hermes 4.3 36B needs ~32.3 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~27 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 32.3 GB, 27.3 tok/s, Tight fit
32.3 GB required34.6 GB available
93% VRAM used
Fit status
Tight fit
Decode
27.3 tok/s
TTFT
7079 ms
Safe context
25K
Memory
32.3 GB / 34.6 GB
Memory breakdown
Weights22.0 GB
KV Cache4.2 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsHermes 4.3 36B on MacBook Pro M4 Max 48GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 27.3 tok/s decode · 7.1s TTFT (warm) · 68 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 27.3 tok/s | 3861 ms | 25K |
| Coding | C | Tight fit | 27.3 tok/s | 7079 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 | 25K |
Quantization options
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 | 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 |
Get started
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-Optionen
Hardware, die Hermes 4.3 36B gut ausführt
MacBook Pro M3 Max 64GBBestes Preis-Leistungs-Verhältnis
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.10.9 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
MacBook Pro M4 Max 64GBApple-Upgrade
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.27.3 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Frequently asked questions
Can MacBook Pro M4 Max 48GB run Hermes 4.3 36B?
Yes, MacBook Pro M4 Max 48GB can run Hermes 4.3 36B with a C grade (Tight fit). Expected decode speed: 27.3 tok/s.
How much VRAM does Hermes 4.3 36B need?
Hermes 4.3 36B (36B parameters) requires approximately 32.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Hermes 4.3 36B?
The recommended quantization for Hermes 4.3 36B is Q4_K_M, which balances quality and memory efficiency.
What speed will Hermes 4.3 36B run at on MacBook Pro M4 Max 48GB?
On MacBook Pro M4 Max 48GB, Hermes 4.3 36B achieves approximately 27.3 tokens per second decode speed with a time-to-first-token of 7079ms using Q4_K_M quantization.
Can MacBook Pro M4 Max 48GB run Hermes 4.3 36B for coding?
For coding workloads, Hermes 4.3 36B on MacBook Pro M4 Max 48GB receives a C grade with 27.3 tok/s and 25K context.
What context window can Hermes 4.3 36B use on MacBook Pro M4 Max 48GB?
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.
What should I upgrade first if Hermes 4.3 36B feels slow on MacBook Pro M4 Max 48GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M4 Max 48GB as fast as VRAM for Hermes 4.3 36B?
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.
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