Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on Radeon PRO W7700 16GB?

YES — Runs Great

C54Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.7 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.7 GB, 39.8 tok/s, Runs well
12.7 GB required16.0 GB available
79% VRAM used

Fit status

Runs well

Decode

39.8 tok/s

TTFT

4865 ms

Safe context

48K

Memory

12.7 GB / 16.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on Radeon PRO W7700 16GB
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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well39.8 tok/s2654 ms48K
CodingCRuns well39.8 tok/s4865 ms48K
Agentic CodingCTight fit39.8 tok/s7076 ms48K
ReasoningCRuns well39.8 tok/s5750 ms48K
RAGCTight fit39.8 tok/s8846 ms48K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on Radeon PRO W7700 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC49
Q3_K_S
3
6.9 GB
LowC50
NVFP4
4
7.8 GB
MediumC51
Q4_K_M
4
8.5 GB
MediumC51
Q5_K_M
5
10.1 GB
HighC51
Q6_KBest for your GPU
6
11.5 GB
HighC50
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

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 start

Opciones de mejora

Hardware que ejecuta bien GGUF SOLARized GraniStral 14B 1902 YeAM HCT

RX 7900 XT 20GBOpción económica
20 GB VRAM (+4)800 GB/s (+224)
C
Sube la velocidad estimada de decodificación alrededor de un 41%.56.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 41%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

NVIDIARTX A4500 20GBMejor relación calidad-precio
20 GB VRAM (+4)640 GB/s (+64)
C
Sube la velocidad estimada de decodificación alrededor de un 47%.58.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 47%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,000 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, Radeon PRO W7700 16GB 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 12.7 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 W7700 16GB?

On Radeon PRO W7700 16GB, 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 W7700 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on Radeon PRO W7700 16GB receives a C grade with 39.8 tok/s and 48K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 48K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon PRO W7700 16GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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