GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~24.9 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~28 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
28.1 tok/s
TTFT
6889 ms
Safe context
672K
Memory
24.9 GB / 92.2 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 | 28.1 tok/s | 3758 ms | 672K |
| Coding | C | Runs well | 28.1 tok/s | 6889 ms | 672K |
| Agentic Coding | C | Runs well | 28.1 tok/s | 10020 ms | 672K |
| Reasoning | C | Runs well | 28.1 tok/s | 8141 ms | 672K |
| RAG | C | Runs well | 28.1 tok/s | 12525 ms | 672K |
Inference speed
Estimated decode speed (tokens/sec) for GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 140.6 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 89.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 80.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 76.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 75.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 65.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 54.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 35.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 35.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 33.2 | Offloads |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 19.5 | Offloads |
| RTX 4060 8GB | 8 GB | Q4_K_M | 7.2 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | D39 |
Q3_K_S | 3 | 6.9 GB | Low | D39 |
NVFP4 | 4 | 7.8 GB | Medium | D39 |
Q4_K_M | 4 | 8.5 GB | Medium | D39 |
Q5_K_M | 5 | 10.1 GB | High | D39 |
Q6_K | 6 | 11.5 GB | High | D39 |
Q8_0 | 8 | 15.0 GB | Very High | D40 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C42 |
Copy-paste commands to run GGUF SOLARized GraniStral 14B 1902 YeAM HCT on your machine.
Run
lms load hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct && lms server startUpgrade options
Raises estimated decode speed by about 132%.
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Raises estimated decode speed by about 598%.
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
Yes, MacBook Pro M3 Max 128GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 28.1 tok/s.
GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B parameters) requires approximately 24.9 GB of memory with Q4_K_M quantization.
The recommended quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 128GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 28.1 tokens per second decode speed with a time-to-first-token of 6889ms using Q4_K_M quantization.
For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on MacBook Pro M3 Max 128GB receives a C grade with 28.1 tok/s and 672K context.
On MacBook Pro M3 Max 128GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 672K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Max 128GB 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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