Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 3080 10GB?

YES — With NVFP4

C41Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~11.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With NVFP4 quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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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.

GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q4_K_M needs 12.4 GB — too much for RTX 3080 10GB (10.0 GB). Runs at NVFP4 (11.7 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.4 GB, exceeds 10.0 GB available
12.4 GB required10.0 GB available
124% VRAM needed

2.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

32.4 tok/s

TTFT

5983 ms

Safe context

4K

Memory

12.4 GB / 10.0 GB

Offload

20%

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom1.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 3080 10GB
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: 32.4 tok/s decode · 6.0s TTFT (warm) · 81 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCVery compromised (needs ~1.2 GB host RAM)37.4 tok/s2825 ms4K
CodingFToo heavy32.4 tok/s5983 ms4K
Agentic CodingFToo heavy24.9 tok/s11308 ms4K
ReasoningFToo heavy32.4 tok/s7070 ms4K
RAGFToo heavy24.9 tok/s14135 ms4K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC52
Q3_K_SBest for your GPU
3
6.9 GB
LowC52
NVFP4
4
7.8 GB
MediumF0
Q4_K_M
4
8.5 GB
MediumF0
Q5_K_M
5
10.1 GB
HighF0
Q6_K
6
11.5 GB
HighF0
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

Opções de upgrade

Hardware que roda bem GGUF SOLARized GraniStral 14B 1902 YeAM HCT

NVIDIARTX 3060 12GBOpção econômica
12 GB VRAM (+2)
C
Makes the model fit on the accelerator instead of staying completely out of reach.18.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMelhor custo-benefício
16 GB VRAM (+6)
C
Makes the model fit on the accelerator instead of staying completely out of reach.32.5 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBUpgrade NVIDIA
16 GB VRAM (+6)
C
Makes the model fit on the accelerator instead of staying completely out of reach.24.6 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can RTX 3080 10GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX 3080 10GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT at NVFP4 quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 12.4 GB which exceeds available memory, but at NVFP4 it needs only 11.7 GB. Expected decode speed: 41.8 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 12.4 GB at Q4_K_M quantization. On RTX 3080 10GB, it fits at NVFP4 using 11.7 GB.

What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

The recommended quantization is Q4_K_M, but on RTX 3080 10GB the best fitting quantization is NVFP4, which uses 11.7 GB.

What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on RTX 3080 10GB?

On RTX 3080 10GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 41.8 tokens per second decode speed with a time-to-first-token of 4628ms using NVFP4 quantization.

Can RTX 3080 10GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 3080 10GB receives a F grade with 32.4 tok/s and 4K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX 3080 10GB?

On RTX 3080 10GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if GGUF SOLARized GraniStral 14B 1902 YeAM HCT feels slow on RTX 3080 10GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3080 10GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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