Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX A4000 16GB?

YES — Runs Great

C54Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~13.0 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 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) — 13.0 GB, 36.7 tok/s, Runs well

13.0 GB required16.0 GB available

81% VRAM used

Fit status

Runs well

Decode

36.7 tok/s

TTFT

5272 ms

Safe context

45K

Memory

13.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX A4000 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: 36.7 tok/s decode · 5.3s TTFT (warm) · 92 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	36.7 tok/s	2876 ms	45K
Coding	C	Runs well	36.7 tok/s	5272 ms	45K
Agentic Coding	C	Tight fit	36.7 tok/s	7669 ms	45K
Reasoning	C	Runs well	36.7 tok/s	6231 ms	45K
RAG	C	Tight fit	36.7 tok/s	9586 ms	45K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX A4000 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 4090 24GBBudget pick

24 GB VRAM (+8)1008 GB/s (+560)

Raises estimated decode speed by about 144%.89.7 tok/s decode

Raises estimated decode speed by about 144%.

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

~$1,599 MSRP

RTX 3090 Ti 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+560)

Raises estimated decode speed by about 128%.83.8 tok/s decode

Raises estimated decode speed by about 128%.

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

~$1,999 MSRP

RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+192)

Raises estimated decode speed by about 59%.58.5 tok/s decode

Raises estimated decode speed by about 59%.

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

~$2,000 MSRP

Frequently asked questions

Can RTX A4000 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX A4000 16GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 36.7 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 13.0 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 A4000 16GB?

On RTX A4000 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 36.7 tokens per second decode speed with a time-to-first-token of 5272ms using Q4_K_M quantization.

Can RTX A4000 16GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX A4000 16GB receives a C grade with 36.7 tok/s and 45K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX A4000 16GB?

On RTX A4000 16GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 45K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A4000 16GB See all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT

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