MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 99%.39.6 tok/s decode
Raises estimated decode speed by about 99%.
~$2,499 MSRP
SOLAR 10.7B v1.0 needs ~12.1 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~20 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
19.9 tok/s
TTFT
9720 ms
Safe context
155K
Memory
12.1 GB / 23.0 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 | 19.9 tok/s | 5302 ms | 155K |
| Coding | C | Runs well | 19.9 tok/s | 9720 ms | 155K |
| Agentic Coding | C | Runs well | 19.9 tok/s | 14138 ms | 155K |
| Reasoning | C | Runs well | 19.9 tok/s | 11488 ms | 155K |
| RAG | C | Runs well | 19.9 tok/s | 17673 ms | 155K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C45 |
Q3_K_S | 3 | 5.2 GB | Low | C45 |
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 99%.
~$2,499 MSRP
Raises estimated decode speed by about 133%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 257%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M1 Pro 32GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 19.9 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 12.1 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 M1 Pro 32GB, SOLAR 10.7B v1.0 achieves approximately 19.9 tokens per second decode speed with a time-to-first-token of 9720ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on MacBook Pro M1 Pro 32GB receives a C grade with 19.9 tok/s and 155K context.
On MacBook Pro M1 Pro 32GB, SOLAR 10.7B v1.0 can safely use up to 155K 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-m1-pro-32gb" 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 |
| C46 |
Q4_K_M | 4 | 6.5 GB | Medium | C46 |
Q5_K_M | 5 | 7.7 GB | High | C47 |
Q6_K | 6 | 8.8 GB | High | C48 |
Q8_0Best for your GPU | 8 | 11.4 GB | Very High | C50 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Not always. MacBook Pro M1 Pro 32GB 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.