RTX PRO 5000 Blackwell 48GBGünstige Wahl1344 GB/s (+544)
C
Raises estimated decode speed by about 157%.132.2 tok/s Dekodierung
Raises estimated decode speed by about 157%.
ca. $4,999 MSRP
Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on Mac Studio M1 Ultra 64GB?
YES — Runs Great
C48Usable
Estimated from fit model
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~18.0 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 18.0 GB, 51.5 tok/s, Runs well
18.0 GB required46.1 GB available
39% VRAM used
Fit status
Runs well
Decode
51.5 tok/s
TTFT
3758 ms
Safe context
290K
Memory
18.0 GB / 46.1 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom6.9 GB
See how fast it feels
See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on Mac Studio M1 Ultra 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 51.5 tok/s decode · 3.8s TTFT (warm) · 129 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 51.5 tok/s | 2050 ms | 290K |
| Coding | C | Runs well | 51.5 tok/s | 3758 ms | 290K |
| Agentic Coding | C | Runs well | 51.5 tok/s | 5466 ms | 290K |
| Reasoning | C | Runs well | 51.5 tok/s | 4441 ms | 290K |
| RAG | C | Runs well | 51.5 tok/s | 6832 ms | 290K |
Quantization options
How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C42 |
Q3_K_S | 3 | 6.9 GB | Low | C42 |
NVFP4 | 4 | 7.8 GB | Medium | C42 |
Q4_K_M | 4 | 8.5 GB | Medium | C42 |
Q5_K_M | 5 | 10.1 GB | High | C43 |
Q6_K | 6 | 11.5 GB | High | C43 |
Q8_0 | 8 | 15.0 GB | Very High | C44 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C48 |
Get started
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 startUpgrade-Optionen
Hardware, die GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV gut ausführt
Frequently asked questions
Can Mac Studio M1 Ultra 64GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
Yes, Mac Studio M1 Ultra 64GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 51.5 tok/s.
How much VRAM does GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV need?
GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 18.0 GB of memory with Q4_K_M quantization.
What is the best quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.
What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on Mac Studio M1 Ultra 64GB?
On Mac Studio M1 Ultra 64GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 51.5 tokens per second decode speed with a time-to-first-token of 3758ms using Q4_K_M quantization.
Can Mac Studio M1 Ultra 64GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on Mac Studio M1 Ultra 64GB receives a C grade with 51.5 tok/s and 290K context.
What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on Mac Studio M1 Ultra 64GB?
On Mac Studio M1 Ultra 64GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 290K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M1 Ultra 64GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?
Not always. Mac Studio M1 Ultra 64GB 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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