SOLAR 10.7B v1.0 needs ~13.9 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~32 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
Fit status
Runs well
Decode
28.3 tok/s
TTFT
6831 ms
Safe context
280K
Memory
13.9 GB / 34.6 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 28.3 tok/s | 3726 ms | 280K |
| Coding | C | Runs well | 32.2 tok/s | 6011 ms | 280K |
| Agentic Coding | C | Runs well | 28.3 tok/s | 9936 ms | 280K |
| Reasoning | C | Runs well | 28.3 tok/s | 8073 ms | 280K |
| RAG | C | Runs well | 28.3 tok/s | 12420 ms | 280K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C43 |
Q3_K_S | 3 | 5.2 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run SOLAR 10.7B v1.0 on your machine.
Run
lms load hf-mradermacher--solar-10-7b-v1-0-gguf && lms server startUpgrade options
Raises estimated decode speed by about 151%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 201%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 48GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 32.2 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 13.9 GB of memory with Q4_K_M quantization.
The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 48GB, SOLAR 10.7B v1.0 achieves approximately 32.2 tokens per second decode speed with a time-to-first-token of 6011ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on MacBook Pro M4 Pro 48GB receives a C grade with 32.2 tok/s and 280K context.
On MacBook Pro M4 Pro 48GB, SOLAR 10.7B v1.0 can safely use up to 280K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-10-7b-v1-0-gguf-on-m4-pro-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
6.0 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 6.5 GB | Medium | C43 |
Q5_K_M | 5 | 7.7 GB | High | C44 |
Q6_K | 6 | 8.8 GB | High | C44 |
Q8_0 | 8 | 11.4 GB | Very High | C45 |
F16Best for your GPU | 16 | 21.9 GB | Maximum | C49 |
Not always. MacBook Pro M4 Pro 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.