Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on RTX A4500 20GB?

YES — Runs Great

C54Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~13.4 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 13.4 GB, 58.5 tok/s, Runs well
13.4 GB required20.0 GB available
67% VRAM used

Fit status

Runs well

Decode

58.5 tok/s

TTFT

3312 ms

Safe context

81K

Memory

13.4 GB / 20.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom2.0 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on RTX A4500 20GB
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: 58.5 tok/s decode · 3.3s TTFT (warm) · 146 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 well58.5 tok/s1807 ms81K
CodingCRuns well58.5 tok/s3312 ms81K
Agentic CodingBRuns well58.5 tok/s4817 ms81K
ReasoningCRuns well58.5 tok/s3914 ms81K
RAGBRuns well58.5 tok/s6022 ms81K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC47
Q3_K_S
3
6.9 GB
LowC48
NVFP4
4
7.8 GB
MediumC49
Q4_K_M
4
8.5 GB
MediumC49
Q5_K_M
5
10.1 GB
HighC51
Q6_K
6
11.5 GB
HighC50
Q8_0Best for your GPU
8
15.0 GB
Very HighC50
F16
16
28.7 GB
MaximumF0

Get started

Copy-paste commands to run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server start

Frequently asked questions

Can RTX A4500 20GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, RTX A4500 20GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 58.5 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV need?

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on RTX A4500 20GB?

On RTX A4500 20GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 58.5 tokens per second decode speed with a time-to-first-token of 3312ms using Q4_K_M quantization.

Can RTX A4500 20GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on RTX A4500 20GB receives a C grade with 58.5 tok/s and 81K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on RTX A4500 20GB?

On RTX A4500 20GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 81K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A4500 20GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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