SOLAR 10.7B Instruct v1.0 uncensored needs ~10.4 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 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
Tight fit
Decode
19.9 tok/s
TTFT
9720 ms
Safe context
30K
Memory
10.4 GB / 11.5 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 | Tight fit | 19.9 tok/s | 5302 ms | 30K |
| Coding | C | Tight fit | 19.9 tok/s | 9720 ms | 30K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 19.3 tok/s | 14569 ms | 30K |
| Reasoning | C | Tight fit | 19.9 tok/s | 11488 ms | 30K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 19.3 tok/s | 18211 ms | 30K |
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on MacBook Pro M1 Pro 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 | C53 |
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 | C52 |
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 10.7B Instruct v1.0 uncensored on your machine.
Run
lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server startOpciones de mejora
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,099 MSRP
Sube la velocidad estimada de decodificación alrededor de un 42%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
~$1,999 MSRP
Yes, MacBook Pro M1 Pro 16GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Tight fit). Expected decode speed: 19.9 tok/s.
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.
The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 16GB, SOLAR 10.7B Instruct v1.0 uncensored 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 Instruct v1.0 uncensored on MacBook Pro M1 Pro 16GB receives a C grade with 19.9 tok/s and 30K context.
On MacBook Pro M1 Pro 16GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 30K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M1 Pro 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.
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<iframe src="https://willitrunai.com/embed/hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf-on-m1-pro-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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