Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 4070 Ti Super 16GB?

YES — Runs Great

B55Good

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~12.7 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~66 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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, 66.1 tok/s, Runs well

12.7 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

66.1 tok/s

TTFT

2929 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 4070 Ti Super 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: 66.1 tok/s decode · 2.9s TTFT (warm) · 165 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	B	Runs well	66.1 tok/s	1598 ms	48K
Coding	B	Runs well	66.1 tok/s	2929 ms	48K
Agentic Coding	C	Tight fit	66.1 tok/s	4260 ms	48K
Reasoning	B	Runs well	66.1 tok/s	3462 ms	48K
RAG	C	Tight fit	66.1 tok/s	5325 ms	48K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 4070 Ti Super 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

Frequently asked questions

Can RTX 4070 Ti Super 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX 4070 Ti Super 16GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a B grade (Runs well). Expected decode speed: 66.1 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 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 66.1 tokens per second decode speed with a time-to-first-token of 2929ms using Q4_K_M quantization.

Can RTX 4070 Ti Super 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 4070 Ti Super 16GB receives a B grade with 66.1 tok/s and 48K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX 4070 Ti Super 16GB?

See all results for RTX 4070 Ti Super 16GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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