MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)
C
Este setup está bastante equilibrado para este modelo.35.4 tok/s decodificación
~$2,499 MSRP
Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on Radeon Pro W7800 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~14.3 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 14.3 GB, 39.8 tok/s, Runs well
14.3 GB required32.0 GB available
45% VRAM used
Fit status
Runs well
Decode
39.8 tok/s
TTFT
4865 ms
Safe context
189K
Memory
14.3 GB / 32.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on Radeon Pro W7800 32GB
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: 39.8 tok/s decode · 4.9s TTFT (warm) · 100 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 39.8 tok/s | 2654 ms | 189K |
| Coding | C | Runs well | 39.8 tok/s | 4865 ms | 189K |
| Agentic Coding | C | Runs well | 39.8 tok/s | 7076 ms | 189K |
| Reasoning | C | Runs well | 39.8 tok/s | 5750 ms | 189K |
| RAG | C | Runs well | 39.8 tok/s | 8846 ms | 189K |
Quantization options
How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C43 |
Q3_K_S | 3 | 6.9 GB | Low | C44 |
NVFP4 | 4 | 7.8 GB | Medium | C44 |
Q4_K_M | 4 | 8.5 GB | Medium | C45 |
Q5_K_M | 5 | 10.1 GB | High | C45 |
Q6_K | 6 | 11.5 GB | High | C46 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | C48 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
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 startOpciones de mejora
Hardware que ejecuta bien GGUF SOLARized GraniStral 14B 1902 YeAM HCT
RTX PRO 5000 Blackwell 48GBMejor relación calidad-precio48 GB VRAM (+16)1344 GB/s (+768)
C
Sube la velocidad estimada de decodificación alrededor de un 232%.132.2 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 232%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$4,999 MSRP
Frequently asked questions
Can Radeon Pro W7800 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?
Yes, Radeon Pro W7800 32GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 39.8 tok/s.
How much VRAM does GGUF SOLARized GraniStral 14B 1902 YeAM HCT need?
GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.
What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?
The recommended quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT is Q4_K_M, which balances quality and memory efficiency.
What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on Radeon Pro W7800 32GB?
On Radeon Pro W7800 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 39.8 tokens per second decode speed with a time-to-first-token of 4865ms using Q4_K_M quantization.
Can Radeon Pro W7800 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on Radeon Pro W7800 32GB receives a C grade with 39.8 tok/s and 189K context.
What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on Radeon Pro W7800 32GB?
On Radeon Pro W7800 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 189K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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