SOLAR 10.7B v1.0 needs ~19.0 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~36 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
35.5 tok/s
TTFT
5447 ms
Safe context
655K
Memory
19.0 GB / 69.1 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 | 35.5 tok/s | 2971 ms | 655K |
| Coding | C | Runs well | 35.5 tok/s | 5447 ms | 655K |
| Agentic Coding | C | Runs well | 35.5 tok/s | 7922 ms | 655K |
| Reasoning | C | Runs well | 35.5 tok/s | 6437 ms | 655K |
| RAG | C | Runs well | 35.5 tok/s | 9903 ms | 655K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | D40 |
Q3_K_S | 3 | 5.2 GB | Low | D40 |
NVFP4 | 4 | 6.0 GB | Medium | D40 |
Q4_K_M | 4 | 6.5 GB | Medium | D40 |
Q5_K_M | 5 | 7.7 GB | High | C40 |
Q6_K | 6 | 8.8 GB | High | C40 |
Q8_0 | 8 | 11.4 GB | Very High | C41 |
F16Best for your GPU | 16 | 21.9 GB | Maximum | C43 |
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-Optionen
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Raises estimated decode speed by about 90%.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Raises estimated decode speed by about 31%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 35.5 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 19.0 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 M2 Max 96GB, SOLAR 10.7B v1.0 achieves approximately 35.5 tokens per second decode speed with a time-to-first-token of 5447ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on MacBook Pro M2 Max 96GB receives a C grade with 35.5 tok/s and 655K context.
On MacBook Pro M2 Max 96GB, SOLAR 10.7B v1.0 can safely use up to 655K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M2 Max 96GB 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.
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-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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