Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on NVIDIA A10 24GB?

YES — Runs Great

C52Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~13.8 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~55 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) — 13.8 GB, 54.8 tok/s, Runs well
13.8 GB required24.0 GB available
58% VRAM used

Fit status

Runs well

Decode

54.8 tok/s

TTFT

3533 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 2102 YeAM HCT 32QKV on NVIDIA A10 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: 54.8 tok/s decode · 3.5s TTFT (warm) · 137 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 well54.8 tok/s1927 ms116K
CodingCRuns well54.8 tok/s3533 ms116K
Agentic CodingCRuns well54.8 tok/s5139 ms116K
ReasoningCRuns well54.8 tok/s4175 ms116K
RAGCRuns well54.8 tok/s6423 ms116K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on NVIDIA A10 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 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 NVIDIA A10 24GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, NVIDIA A10 24GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 54.8 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 13.8 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 NVIDIA A10 24GB?

On NVIDIA A10 24GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 54.8 tokens per second decode speed with a time-to-first-token of 3533ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA A10 24GB receives a C grade with 54.8 tok/s and 116K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on NVIDIA A10 24GB?

On NVIDIA A10 24GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 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 A10 24GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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