Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 3090 Ti 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~13.8 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q4_K_M quantization, expect ~84 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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, 83.8 tok/s, Runs well

13.8 GB required24.0 GB available

58% VRAM used

Fit status

Runs well

Decode

83.8 tok/s

TTFT

2310 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 RTX 3090 Ti 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: 83.8 tok/s decode · 2.3s TTFT (warm) · 210 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	83.8 tok/s	1260 ms	116K
Coding	C	Runs well	83.8 tok/s	2310 ms	116K
Agentic Coding	C	Runs well	83.8 tok/s	3360 ms	116K
Reasoning	C	Runs well	83.8 tok/s	2730 ms	116K
RAG	C	Runs well	83.8 tok/s	4200 ms	116K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C45
Q3_K_S	3	6.9 GB	Low	C46
NVFP4	4

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

Frequently asked questions

Can RTX 3090 Ti 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX 3090 Ti 24GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 83.8 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 RTX 3090 Ti 24GB?

On RTX 3090 Ti 24GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 83.8 tokens per second decode speed with a time-to-first-token of 2310ms using Q4_K_M quantization.

Can RTX 3090 Ti 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 3090 Ti 24GB receives a C grade with 83.8 tok/s and 116K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX 3090 Ti 24GB?

On RTX 3090 Ti 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 RTX 3090 Ti 24GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-srs6901--gguf-solarized-granistral-14b-1902-yeam-hct-on-rtx-3090-ti-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: