Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 4060 Ti 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.7 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 12.7 GB, 25.8 tok/s, Runs well

12.7 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

25.8 tok/s

TTFT

7490 ms

Safe context

48K

Memory

12.7 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 4060 Ti 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 65 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	25.8 tok/s	4085 ms	48K
Coding	C	Runs well	25.8 tok/s	7490 ms	48K
Agentic Coding	C	Tight fit	25.8 tok/s	10894 ms	48K
Reasoning	C	Runs well	25.8 tok/s	8851 ms	48K
RAG	C	Tight fit	25.8 tok/s	13617 ms	48K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C49
Q3_K_S	3	6.9 GB	Low	C50
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

Upgrade options

Hardware that runs GGUF SOLARized GraniStral 14B 1902 YeAM HCT well

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+648)

Raises estimated decode speed by about 191%.75.2 tok/s decode

Raises estimated decode speed by about 191%.

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

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+8)1008 GB/s (+720)

Raises estimated decode speed by about 265%.94.2 tok/s decode

Raises estimated decode speed by about 265%.

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

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+384)

Raises estimated decode speed by about 156%.66.1 tok/s decode

Raises estimated decode speed by about 156%.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX 4060 Ti 16GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 25.8 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 12.7 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 4060 Ti 16GB?

On RTX 4060 Ti 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7490ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 4060 Ti 16GB receives a C grade with 25.8 tok/s and 48K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 48K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 16GB 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-4060-ti-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: