Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~16.2 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 16.2 GB, 54.3 tok/s, Runs well
16.2 GB required48.0 GB available
34% VRAM used

Fit status

Runs well

Decode

54.3 tok/s

TTFT

3566 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 1902 YeAM HCT on Quadro RTX 8000 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: 54.3 tok/s decode · 3.6s TTFT (warm) · 136 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well54.3 tok/s1945 ms326K
CodingCRuns well54.3 tok/s3566 ms326K
Agentic CodingCRuns well54.3 tok/s5186 ms326K
ReasoningCRuns well54.3 tok/s4214 ms326K
RAGCRuns well54.3 tok/s6483 ms326K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowC41
Q3_K_S
3
6.9 GB
LowC42
NVFP4
4
7.8 GB
MediumC42
Q4_K_M
4
8.5 GB
MediumC42
Q5_K_M
5
10.1 GB
HighC42
Q6_K
6
11.5 GB
HighC43
Q8_0
8
15.0 GB
Very HighC44
F16Best for your GPU
16
28.7 GB
MaximumC48

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

Opções de upgrade

Hardware que roda bem GGUF SOLARized GraniStral 14B 1902 YeAM HCT

Mac Studio M3 Ultra 96GBOpção econômica
96 GB Unified (+48)819 GB/s (+147)
C
Adds memory headroom for longer context windows and future model growth.65.2 tok/s decodificação

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

~$3,999 MSRP

AMD Instinct MI210 64GBMelhor custo-benefício
64 GB VRAM (+16)1638 GB/s (+966)
C
Raises estimated decode speed by about 140%.130.4 tok/s decodificação

Raises estimated decode speed by about 140%.

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

~$10,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, Quadro RTX 8000 48GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT with a C grade (Runs well). Expected decode speed: 54.3 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 16.2 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3566ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on Quadro RTX 8000 48GB receives a C grade with 54.3 tok/s and 326K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT 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 Quadro RTX 8000 48GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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