Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 4500 Ada 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~13.8 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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, 40.0 tok/s, Runs well

13.8 GB required24.0 GB available

58% VRAM used

Fit status

Runs well

Decode

40.0 tok/s

TTFT

4845 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 4500 Ada 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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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	40.0 tok/s	2642 ms	116K
Coding	C	Runs well	40.0 tok/s	4845 ms	116K
Agentic Coding	C	Runs well	40.0 tok/s	7047 ms	116K
Reasoning	C	Runs well	40.0 tok/s	5725 ms	116K
RAG	C	Runs well	40.0 tok/s	8808 ms	116K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 4500 Ada 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	7.8 GB	Medium	C47
Q4_K_M	4	8.5 GB	Medium	C47
Q5_K_M	5	10.1 GB	High	C48
Q6_K	6	11.5 GB	High	C49
Q8_0Best for your GPU	8	15.0 GB	Very High	C50
F16	16	28.7 GB	Maximum	F0

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 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1360)

Raises estimated decode speed by about 251%.140.6 tok/s decode

Raises estimated decode speed by about 251%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+464)

Raises estimated decode speed by about 120%.88.1 tok/s decode

Raises estimated decode speed by about 120%.

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

~$2,499 MSRP

Frequently asked questions

Can RTX 4500 Ada 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX 4500 Ada 24GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 40.0 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 4500 Ada 24GB?

On RTX 4500 Ada 24GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4845ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 4500 Ada 24GB receives a C grade with 40.0 tok/s and 116K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 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 4500 Ada 24GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

40.0 tok/s

2642 ms

116K

Coding

Runs well

40.0 tok/s

4845 ms

116K

Agentic Coding

Runs well

40.0 tok/s

7047 ms

116K

Reasoning

Runs well

40.0 tok/s

5725 ms

116K

RAG

Runs well

40.0 tok/s

8808 ms

116K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 4500 Ada 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	7.8 GB	Medium	C47
Q4_K_M	4	8.5 GB	Medium	C47
Q5_K_M	5	10.1 GB	High	C48
Q6_K	6	11.5 GB	High	C49
Q8_0Best for your GPU	8	15.0 GB	Very High	C50
F16	16	28.7 GB	Maximum	F0