SOLAR 10.7B v1.0 needs ~11.3 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~12 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
11.6 tok/s
TTFT
16620 ms
Safe context
93K
Memory
11.3 GB / 17.3 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 | 11.6 tok/s | 9065 ms | 93K |
| Coding | C | Runs well | 11.6 tok/s | 16620 ms | 93K |
| Agentic Coding | C | Runs well | 11.6 tok/s | 24174 ms | 93K |
| Reasoning | C | Runs well | 11.6 tok/s | 19641 ms | 93K |
| RAG | C | Runs well | 11.6 tok/s | 30218 ms | 93K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C47 |
Q3_K_S | 3 | 5.2 GB | Low | C48 |
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 206%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 191%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 241%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Air M4 24GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 11.6 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 11.3 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 Air M4 24GB, SOLAR 10.7B v1.0 achieves approximately 11.6 tokens per second decode speed with a time-to-first-token of 16620ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on MacBook Air M4 24GB receives a C grade with 11.6 tok/s and 93K context.
On MacBook Air M4 24GB, SOLAR 10.7B v1.0 can safely use up to 93K 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-air-24gb" 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 |
| C49 |
Q4_K_M | 4 | 6.5 GB | Medium | C49 |
Q5_K_M | 5 | 7.7 GB | High | C50 |
Q6_K | 6 | 8.8 GB | High | C51 |
Q8_0Best for your GPU | 8 | 11.4 GB | Very High | C50 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Not always. MacBook Air M4 24GB 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.