Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on NVIDIA L4 24GB?

YES — Runs Great

C49Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~13.8 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 13.8 GB, 22.8 tok/s, Runs well
13.8 GB required24.0 GB available
58% VRAM used

Fit status

Runs well

Decode

22.8 tok/s

TTFT

8479 ms

Safe context

116K

Memory

13.8 GB / 24.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on NVIDIA L4 24GB
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: 22.8 tok/s decode · 8.5s TTFT (warm) · 57 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 well22.8 tok/s4625 ms116K
CodingCRuns well22.8 tok/s8479 ms116K
Agentic CodingCRuns well22.8 tok/s12333 ms116K
ReasoningCRuns well22.8 tok/s10020 ms116K
RAGCRuns well22.8 tok/s15416 ms116K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC45
Q3_K_S
3
6.9 GB
LowC46
NVFP4
4
7.8 GB
MediumC47
Q4_K_M
4
8.5 GB
MediumC47
Q5_K_M
5
10.1 GB
HighC48
Q6_K
6
11.5 GB
HighC49
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 1902 YeAM HCT on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct && lms server start

Upgrade-Optionen

Hardware, die GGUF SOLARized GraniStral 14B 1902 YeAM HCT gut ausführt

NVIDIARTX 5090 32GBGünstige Wahl
32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 517%.140.6 tok/s Dekodierung

Raises estimated decode speed by about 517%.

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

ca. $1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis
32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 286%.88.1 tok/s Dekodierung

Raises estimated decode speed by about 286%.

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

ca. $2,499 MSRP

NVIDIARTX 5000 Ada 32GBNVIDIA-Upgrade
32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 137%.54 tok/s Dekodierung

Raises estimated decode speed by about 137%.

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

ca. $4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, NVIDIA L4 24GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 22.8 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 13.8 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 22.8 tokens per second decode speed with a time-to-first-token of 8479ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on NVIDIA L4 24GB receives a C grade with 22.8 tok/s and 116K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 116K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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