RTX 4080 Super 16GB最大飞跃736 GB/s (+616)
B
Raises estimated decode speed by about 747%.98.3 tok/s 解码
Raises estimated decode speed by about 747%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Can Nous Hermes 2 SOLAR 10.7B run on MacBook Pro M4 16GB?
YES — Tight Fit
C47Usable
Estimated — low-sample bucket· few comparable runs
Nous Hermes 2 SOLAR 10.7B needs ~10.4 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~12 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 10.4 GB, 11.6 tok/s, Tight fit
10.4 GB required11.5 GB available
90% VRAM used
Fit status
Tight fit
Decode
11.6 tok/s
TTFT
16620 ms
Safe context
30K
Memory
10.4 GB / 11.5 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsNous Hermes 2 SOLAR 10.7B on MacBook Pro M4 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: 11.6 tok/s decode · 16.6s TTFT (warm) · 29 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 | Tight fit | 11.6 tok/s | 9065 ms | 30K |
| Coding | C | Tight fit | 11.6 tok/s | 16620 ms | 30K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 11.3 tok/s | 24910 ms | 30K |
| Reasoning | C | Tight fit | 11.6 tok/s | 19641 ms | 30K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 11.3 tok/s | 31137 ms | 30K |
Quantization options
How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C51 |
Q3_K_S | 3 | 5.2 GB | Low | C53 |
NVFP4 | 4 | 6.0 GB | Medium | C52 |
Q4_K_M | 4 | 6.5 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 7.7 GB | High | C52 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Get started
Copy-paste commands to run Nous Hermes 2 SOLAR 10.7B on your machine.
Run
lms load hf-thebloke--nous-hermes-2-solar-10-7b-gguf && lms server start升级选项
能流畅运行 Nous Hermes 2 SOLAR 10.7B 的硬件
RTX 4080 Super 16GB最大飞跃MacBook Air M4 24GB经济之选
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.11.6 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Pro M3 24GB最佳性价比
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.10.4 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Air M3 24GBApple 升级
24 GB Unified (+8)
C
Adds memory headroom for longer context windows and future model growth.10.4 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Frequently asked questions
Can MacBook Pro M4 16GB run Nous Hermes 2 SOLAR 10.7B?
Yes, MacBook Pro M4 16GB can run Nous Hermes 2 SOLAR 10.7B with a C grade (Tight fit). Expected decode speed: 11.6 tok/s.
How much VRAM does Nous Hermes 2 SOLAR 10.7B need?
Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Nous Hermes 2 SOLAR 10.7B?
The recommended quantization for Nous Hermes 2 SOLAR 10.7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Nous Hermes 2 SOLAR 10.7B run at on MacBook Pro M4 16GB?
On MacBook Pro M4 16GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 11.6 tokens per second decode speed with a time-to-first-token of 16620ms using Q4_K_M quantization.
Can MacBook Pro M4 16GB run Nous Hermes 2 SOLAR 10.7B for coding?
For coding workloads, Nous Hermes 2 SOLAR 10.7B on MacBook Pro M4 16GB receives a C grade with 11.6 tok/s and 30K context.
What context window can Nous Hermes 2 SOLAR 10.7B use on MacBook Pro M4 16GB?
On MacBook Pro M4 16GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 30K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 16GB as fast as VRAM for Nous Hermes 2 SOLAR 10.7B?
Not always. MacBook Pro M4 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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