Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on Radeon AI PRO R9700 32GB?

YES — Runs Great

C49Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~14.3 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~44 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) — 14.3 GB, 44.2 tok/s, Runs well
14.3 GB required32.0 GB available
45% VRAM used

Fit status

Runs well

Decode

44.2 tok/s

TTFT

4379 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 AI PRO R9700 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: 44.2 tok/s decode · 4.4s TTFT (warm) · 111 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 well44.2 tok/s2388 ms189K
CodingCRuns well44.2 tok/s4379 ms189K
Agentic CodingCRuns well44.2 tok/s6369 ms189K
ReasoningCRuns well44.2 tok/s5175 ms189K
RAGCRuns well44.2 tok/s7961 ms189K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC43
Q3_K_S
3
6.9 GB
LowC44
NVFP4
4
7.8 GB
MediumC44
Q4_K_M
4
8.5 GB
MediumC45
Q5_K_M
5
10.1 GB
HighC45
Q6_K
6
11.5 GB
HighC46
Q8_0Best for your GPU
8
15.0 GB
Very HighC48
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

Opções de upgrade

Hardware que roda bem GGUF SOLARized GraniStral 14B 1902 YeAM HCT

NVIDIANVIDIA A100 40GBOpção econômica
40 GB VRAM (+8)1555 GB/s (+915)
C
Raises estimated decode speed by about 246%.153 tok/s decodificação

Raises estimated decode speed by about 246%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, Radeon AI PRO R9700 32GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 44.2 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 AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 44.2 tokens per second decode speed with a time-to-first-token of 4379ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on Radeon AI PRO R9700 32GB receives a C grade with 44.2 tok/s and 189K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 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.

See all results for Radeon AI PRO R9700 32GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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