SOLAR 10.7B Instruct v1.0 uncensored needs ~11.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~10 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
10.0 tok/s
TTFT
19440 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 | 10.0 tok/s | 10604 ms | 93K |
| Coding | C | Runs well | 10.0 tok/s | 19440 ms | 93K |
| Agentic Coding | C | Runs well | 10.0 tok/s | 28277 ms | 93K |
| Reasoning | C | Runs well | 10.0 tok/s | 22975 ms | 93K |
| RAG | C | Runs well | 10.0 tok/s | 35346 ms | 93K |
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on Mac mini M2 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 | 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 | C51 |
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
Sube la velocidad estimada de decodificación alrededor de un 255%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 237%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 296%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Yes, Mac mini M2 24GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 10.0 tok/s.
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 11.3 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 Mac mini M2 24GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 10.0 tokens per second decode speed with a time-to-first-token of 19440ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on Mac mini M2 24GB receives a C grade with 10.0 tok/s and 93K context.
On Mac mini M2 24GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 93K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac mini M2 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.
Paste this snippet into any page to show a live fit card.
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