Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX PRO 4500 Blackwell 32GB?

YES — Runs Great

C51Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~14.6 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~88 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 14.6 GB, 88.1 tok/s, Runs well
14.6 GB required32.0 GB available
46% VRAM used

Fit status

Runs well

Decode

88.1 tok/s

TTFT

2197 ms

Safe context

186K

Memory

14.6 GB / 32.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX PRO 4500 Blackwell 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: 88.1 tok/s decode · 2.2s TTFT (warm) · 220 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 well88.1 tok/s1198 ms186K
CodingCRuns well88.1 tok/s2197 ms186K
Agentic CodingCRuns well88.1 tok/s3195 ms186K
ReasoningCRuns well88.1 tok/s2596 ms186K
RAGCRuns well88.1 tok/s3994 ms186K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX PRO 4500 Blackwell 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

Frequently asked questions

Can RTX PRO 4500 Blackwell 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX PRO 4500 Blackwell 32GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 88.1 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 14.6 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 RTX PRO 4500 Blackwell 32GB?

On RTX PRO 4500 Blackwell 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 88.1 tokens per second decode speed with a time-to-first-token of 2197ms using Q4_K_M quantization.

Can RTX PRO 4500 Blackwell 32GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX PRO 4500 Blackwell 32GB receives a C grade with 88.1 tok/s and 186K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX PRO 4500 Blackwell 32GB?

On RTX PRO 4500 Blackwell 32GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 186K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 4500 Blackwell 32GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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