RX 7900 XT 20GBGünstige Wahl
800 GB/s (+527)
C
Raises estimated decode speed by about 116%.98 tok/s Dekodierung
Raises estimated decode speed by about 116%.
ca. $899 MSRP
Can Hermes 2 Pro Mistral 7B run on MacBook Pro M4 Pro 24GB?
YES — Runs Great
C50Usable
Estimated — low-sample bucket· few comparable runs
Hermes 2 Pro Mistral 7B needs ~8.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~45 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 8.6 GB, 45.3 tok/s, Runs well
8.6 GB required17.3 GB available
50% VRAM used
Fit status
Runs well
Decode
45.3 tok/s
TTFT
4275 ms
Safe context
186K
Memory
8.6 GB / 17.3 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsHermes 2 Pro Mistral 7B on MacBook Pro M4 Pro 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 45.3 tok/s | 2332 ms | 186K |
| Coding | C | Runs well | 45.3 tok/s | 4275 ms | 186K |
| Agentic Coding | C | Runs well | 45.3 tok/s | 6218 ms | 186K |
| Reasoning | C | Runs well | 45.3 tok/s | 5052 ms | 186K |
| RAG | C | Runs well | 45.3 tok/s | 7772 ms | 186K |
Quantization options
How Hermes 2 Pro Mistral 7B (7B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C46 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
NVFP4 | 4 | 3.9 GB | Medium | C47 |
Q4_K_M | 4 | 4.3 GB | Medium | C47 |
Q5_K_M | 5 | 5.0 GB | High | C48 |
Q6_K | 6 | 5.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C50 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Hermes 2 Pro Mistral 7B on your machine.
Run
lms load hf-nousresearch--hermes-2-pro-mistral-7b-gguf && lms server startUpgrade-Optionen
Hardware, die Hermes 2 Pro Mistral 7B gut ausführt
Frequently asked questions
Can MacBook Pro M4 Pro 24GB run Hermes 2 Pro Mistral 7B?
Yes, MacBook Pro M4 Pro 24GB can run Hermes 2 Pro Mistral 7B with a C grade (Runs well). Expected decode speed: 45.3 tok/s.
How much VRAM does Hermes 2 Pro Mistral 7B need?
Hermes 2 Pro Mistral 7B (7B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Hermes 2 Pro Mistral 7B?
The recommended quantization for Hermes 2 Pro Mistral 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Hermes 2 Pro Mistral 7B run at on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, Hermes 2 Pro Mistral 7B achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4275ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 24GB run Hermes 2 Pro Mistral 7B for coding?
For coding workloads, Hermes 2 Pro Mistral 7B on MacBook Pro M4 Pro 24GB receives a C grade with 45.3 tok/s and 186K context.
What context window can Hermes 2 Pro Mistral 7B use on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, Hermes 2 Pro Mistral 7B can safely use up to 186K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for Hermes 2 Pro Mistral 7B?
Not always. MacBook Pro M4 Pro 24GB 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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