Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on NVIDIA A40 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~16.2 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 16.2 GB, 63.6 tok/s, Runs well

16.2 GB required48.0 GB available

34% VRAM used

Fit status

Runs well

Decode

63.6 tok/s

TTFT

3046 ms

Safe context

326K

Memory

16.2 GB / 48.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA A40 48GB
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: 63.6 tok/s decode · 3.0s TTFT (warm) · 159 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	63.6 tok/s	1661 ms	326K
Coding	C	Runs well	63.6 tok/s	3046 ms	326K
Agentic Coding	C	Runs well	63.6 tok/s	4430 ms	326K
Reasoning	C	Runs well	63.6 tok/s	3599 ms	326K
RAG	C	Runs well	63.6 tok/s	5537 ms	326K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C41
Q3_K_S	3	6.9 GB	Low	C42
NVFP4	4	7.8 GB	Medium	C42
Q4_K_M	4	8.5 GB	Medium	C42
Q5_K_M	5	10.1 GB	High	C43
Q6_K	6	11.5 GB	High	C43
Q8_0	8	15.0 GB	Very High	C44
F16Best for your GPU	16	28.7 GB	Maximum	C48

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 NVIDIA A40 48GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, NVIDIA A40 48GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 63.6 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 16.2 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 NVIDIA A40 48GB?

On NVIDIA A40 48GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 63.6 tokens per second decode speed with a time-to-first-token of 3046ms using Q4_K_M quantization.

Can NVIDIA A40 48GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA A40 48GB receives a C grade with 63.6 tok/s and 326K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 326K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A40 48GB See all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV

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-2102-yeam-hct-32qkv-on-a40-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: