MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)546 GB/s (+34)
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 W6800 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~14.3 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~34 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, 33.6 tok/s, Runs well
14.3 GB required32.0 GB available
45% VRAM used
Fit status
Runs well
Decode
33.6 tok/s
TTFT
5766 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 W6800 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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 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 | 33.6 tok/s | 3145 ms | 189K |
| Coding | C | Runs well | 33.6 tok/s | 5766 ms | 189K |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8387 ms | 189K |
| Reasoning | C | Runs well | 33.6 tok/s | 6815 ms | 189K |
| RAG | C | Runs well | 33.6 tok/s | 10484 ms | 189K |
Inference speed
GGUF SOLARized GraniStral 14B 1902 YeAM HCT inference speed — tokens per second by GPU & Mac
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.
Quantization options
How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on Radeon Pro W6800 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
Mac Studio M2 Ultra 64GBMejor relación calidad-precio
64 GB Unified (+32)800 GB/s (+288)
C
Sube la velocidad estimada de decodificación alrededor de un 62%.54.3 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 62%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Frequently asked questions
Can Radeon Pro W6800 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?
Yes, Radeon Pro W6800 32GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 33.6 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 W6800 32GB?
On Radeon Pro W6800 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5766ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?
For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on Radeon Pro W6800 32GB receives a C grade with 33.6 tok/s and 189K context.
What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 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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