Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on NVIDIA A2 16GB?

YES — Runs Great

C51Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~13.0 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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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) — 13.0 GB, 18.3 tok/s, Runs well
13.0 GB required16.0 GB available
81% VRAM used

Fit status

Runs well

Decode

18.3 tok/s

TTFT

10598 ms

Safe context

45K

Memory

13.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on NVIDIA A2 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: 18.3 tok/s decode · 10.6s TTFT (warm) · 46 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 well18.3 tok/s5781 ms45K
CodingCRuns well18.3 tok/s10598 ms45K
Agentic CodingCTight fit18.3 tok/s15416 ms45K
ReasoningCRuns well18.3 tok/s12525 ms45K
RAGCTight fit18.3 tok/s19270 ms45K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on NVIDIA A2 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

NVIDIARTX 4000 Ada 20GBOpción económica
20 GB VRAM (+4)360 GB/s (+160)
C
Sube la velocidad estimada de decodificación alrededor de un 80%.32.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 80%.

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

~$1,250 MSRP

NVIDIARTX 3090 24GBMejor relación calidad-precio
24 GB VRAM (+8)936 GB/s (+736)
C
Sube la velocidad estimada de decodificación alrededor de un 319%.76.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 319%.

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

~$1,499 MSRP

NVIDIARTX PRO 4000 Blackwell 24GBMejora NVIDIA
24 GB VRAM (+8)672 GB/s (+472)
C
Sube la velocidad estimada de decodificación alrededor de un 261%.66.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 261%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, NVIDIA A2 16GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 18.3 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 13.0 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 A2 16GB?

On NVIDIA A2 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 18.3 tokens per second decode speed with a time-to-first-token of 10598ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on NVIDIA A2 16GB receives a C grade with 18.3 tok/s and 45K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 45K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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