Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on RTX 4090 24GB?

YES — Runs Great

C54Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~13.8 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~90 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, 89.7 tok/s, Runs well

13.8 GB required24.0 GB available

58% VRAM used

Fit status

Runs well

Decode

89.7 tok/s

TTFT

2158 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 2102 YeAM HCT 32QKV on RTX 4090 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: 89.7 tok/s decode · 2.2s TTFT (warm) · 224 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	89.7 tok/s	1177 ms	116K
Coding	C	Runs well	89.7 tok/s	2158 ms	116K
Agentic Coding	B	Runs well	89.7 tok/s	3139 ms	116K
Reasoning	C	Runs well	89.7 tok/s	2551 ms	116K
RAG	B	Runs well	89.7 tok/s	3924 ms	116K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on RTX 4090 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 2102 YeAM HCT 32QKV on your machine.

Run

lms load hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv && lms server start

Frequently asked questions

Can RTX 4090 24GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, RTX 4090 24GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 89.7 tok/s.

How much VRAM does GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV need?

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.

What is the best quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

The recommended quantization for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV is Q4_K_M, which balances quality and memory efficiency.

What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on RTX 4090 24GB?

On RTX 4090 24GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 89.7 tokens per second decode speed with a time-to-first-token of 2158ms using Q4_K_M quantization.

Can RTX 4090 24GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on RTX 4090 24GB receives a C grade with 89.7 tok/s and 116K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on RTX 4090 24GB?

On RTX 4090 24GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV 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 4090 24GB See all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

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<iframe src="https://willitrunai.com/embed/hf-srs6901--gguf-solarized-granistral-14b-2102-yeam-hct-32qkv-on-rtx-4090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

89.7 tok/s

1177 ms

116K

Coding

Runs well

89.7 tok/s

2158 ms

116K

Agentic Coding

Runs well

89.7 tok/s

3139 ms

116K

Reasoning

Runs well

89.7 tok/s

2551 ms

116K

RAG

Runs well

89.7 tok/s

3924 ms

116K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on RTX 4090 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