RX 7700 XT 12GBコスパ重視
432 GB/s (+232)
C
Raises estimated decode speed by about 85%.60.7 tok/sデコード
Raises estimated decode speed by about 85%.
〜$449 MSRP
Can Hermes 2 Pro Mistral 7B run on MacBook Pro M2 Pro 16GB?
YES — Runs Great
C53Usable
Estimated from fit model
Hermes 2 Pro Mistral 7B needs ~7.7 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 7.7 GB, 32.8 tok/s, Runs well
7.7 GB required11.5 GB available
67% VRAM used
Fit status
Runs well
Decode
32.8 tok/s
TTFT
5905 ms
Safe context
90K
Memory
7.7 GB / 11.5 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsHermes 2 Pro Mistral 7B on MacBook Pro M2 Pro 16GB
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: 32.8 tok/s decode · 5.9s TTFT (warm) · 82 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 | 32.8 tok/s | 3221 ms | 90K |
| Coding | C | Runs well | 32.8 tok/s | 5905 ms | 90K |
| Agentic Coding | C | Runs well | 32.8 tok/s | 8589 ms | 90K |
| Reasoning | C | Runs well | 32.8 tok/s | 6978 ms | 90K |
| RAG | C | Runs well | 32.8 tok/s | 10736 ms | 90K |
Inference speed
Hermes 2 Pro Mistral 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Hermes 2 Pro Mistral 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.5 | Tight |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Hermes 2 Pro Mistral 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 | 3.9 GB | Medium | C51 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
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 startアップグレードオプション
Hermes 2 Pro Mistral 7Bを快適に動かすハードウェア
Frequently asked questions
Can MacBook Pro M2 Pro 16GB run Hermes 2 Pro Mistral 7B?
Yes, MacBook Pro M2 Pro 16GB can run Hermes 2 Pro Mistral 7B with a C grade (Runs well). Expected decode speed: 32.8 tok/s.
How much VRAM does Hermes 2 Pro Mistral 7B need?
Hermes 2 Pro Mistral 7B (7B parameters) requires approximately 7.7 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 M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Hermes 2 Pro Mistral 7B achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 16GB run Hermes 2 Pro Mistral 7B for coding?
For coding workloads, Hermes 2 Pro Mistral 7B on MacBook Pro M2 Pro 16GB receives a C grade with 32.8 tok/s and 90K context.
What context window can Hermes 2 Pro Mistral 7B use on MacBook Pro M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Hermes 2 Pro Mistral 7B can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Hermes 2 Pro Mistral 7B?
Not always. MacBook Pro M2 Pro 16GB 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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