solar finalised finetuned Model 10.7B i1 needs ~10.4 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~6 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
Tight fit
Decode
6.3 tok/s
TTFT
30971 ms
Safe context
30K
Memory
10.4 GB / 11.5 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 6.3 tok/s | 16893 ms | 30K |
| Coding | C | Tight fit | 6.3 tok/s | 30971 ms | 30K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 6.1 tok/s | 46420 ms | 30K |
| Reasoning | C | Tight fit | 6.3 tok/s | 36602 ms | 30K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 6.1 tok/s | 58025 ms | 30K |
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on MacBook Air M1 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 | C52 |
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 | C51 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.
Run
lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server startOpções de upgrade
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
RTX 4080 Super 16GBMaior saltoRaises estimated decode speed by about 1460%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 65%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Yes, MacBook Air M1 16GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 6.3 tok/s.
solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.
The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M1 16GB, solar finalised finetuned Model 10.7B i1 achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30971ms using Q4_K_M quantization.
For coding workloads, solar finalised finetuned Model 10.7B i1 on MacBook Air M1 16GB receives a C grade with 6.3 tok/s and 30K context.
On MacBook Air M1 16GB, solar finalised finetuned Model 10.7B i1 can safely use up to 30K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. MacBook Air M1 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-m1-16gb" 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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