Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on NVIDIA A100 40GB?

YES — Runs Great

C50Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~15.4 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~153 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) — 15.4 GB, 153.0 tok/s, Runs well
15.4 GB required40.0 GB available
39% VRAM used

Fit status

Runs well

Decode

153.0 tok/s

TTFT

1266 ms

Safe context

256K

Memory

15.4 GB / 40.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom4.0 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA A100 40GB
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: 153.0 tok/s decode · 1.3s TTFT (warm) · 382 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well153.0 tok/s690 ms256K
CodingCRuns well153.0 tok/s1266 ms256K
Agentic CodingCRuns well153.0 tok/s1841 ms256K
ReasoningCRuns well153.0 tok/s1496 ms256K
RAGCRuns well153.0 tok/s2301 ms256K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC42
Q3_K_S
3
6.9 GB
LowC43
NVFP4
4
7.8 GB
MediumC43
Q4_K_M
4
8.5 GB
MediumC43
Q5_K_M
5
10.1 GB
HighC44
Q6_K
6
11.5 GB
HighC44
Q8_0
8
15.0 GB
Very HighC46
F16Best for your GPU
16
28.7 GB
MaximumC48

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 A100 40GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, NVIDIA A100 40GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV with a C grade (Runs well). Expected decode speed: 153.0 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 15.4 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 A100 40GB?

On NVIDIA A100 40GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 153.0 tokens per second decode speed with a time-to-first-token of 1266ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA A100 40GB receives a C grade with 153.0 tok/s and 256K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 256K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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