GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~15.0 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~13 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
12.8 tok/s
TTFT
15099 ms
Safe context
123K
Memory
15.0 GB / 25.9 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 | 12.8 tok/s | 8236 ms | 123K |
| Coding | C | Runs well | 12.8 tok/s | 15099 ms | 123K |
| Agentic Coding | C | Runs well | 12.8 tok/s | 21962 ms | 123K |
| Reasoning | C | Runs well | 12.8 tok/s | 17844 ms | 123K |
| RAG | C | Runs well | 12.8 tok/s | 27453 ms | 123K |
How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C45 |
Q3_K_S | 3 | 6.9 GB | Low | C46 |
NVFP4 | 4 | 7.8 GB | Medium | C46 |
Q4_K_M | 4 | 8.5 GB | Medium | C47 |
Q5_K_M | 5 | 10.1 GB | High | C47 |
Q6_K | 6 | 11.5 GB | High | C48 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | C50 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Copy-paste commands to run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on your machine.
Run
lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server startOpções de upgrade
Raises estimated decode speed by about 177%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 120%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 324%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M3 Pro 36GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 12.8 tok/s.
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15099ms using Q4_K_M quantization.
For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on MacBook Pro M3 Pro 36GB receives a C grade with 12.8 tok/s and 123K context.
On MacBook Pro M3 Pro 36GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 123K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Pro 36GB 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-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: