Will It Run AI

Can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run on NVIDIA DGX Spark 128GB?

YES — With F16

C43Usable
Estimated from fit model

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV needs ~44.6 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth
Share:

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.

GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV at Q4_K_M needs 11.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (44.6 GB) with maximum quality. 8 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.4 GB, 19.2 tok/s, Runs well
24.4 GB required108.8 GB available
22% VRAM used

Fit status

Runs well

Decode

19.2 tok/s

TTFT

10094 ms

Safe context

839K

Memory

24.4 GB / 108.8 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom13.1 GB

See how fast it feels

See how fast it feelsGGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA DGX Spark 128GB
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: 19.2 tok/s decode · 10.1s TTFT (warm) · 48 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well19.2 tok/s5506 ms839K
CodingFToo heavy3.5 tok/s56076 ms4K
Agentic CodingCRuns well19.2 tok/s14682 ms839K
ReasoningCRuns well19.2 tok/s11929 ms839K
RAGCRuns well19.2 tok/s18352 ms839K

Quantization options

How GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV (14B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowD39
Q3_K_S
3
6.9 GB
LowD39
NVFP4
4
7.8 GB
MediumD39
Q4_K_M
4
8.5 GB
MediumD39
Q5_K_M
5
10.1 GB
HighD39
Q6_K
6
11.5 GB
HighD39
Q8_0
8
15.0 GB
Very HighD40
F16Best for your GPU
16
28.7 GB
MaximumC42

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

Upgrade-Optionen

Hardware, die GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV gut ausführt

NVIDIANVIDIA H200 141GBGünstige Wahl
141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 921%.196 tok/s Dekodierung

Raises estimated decode speed by about 921%.

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

ca. $30,000 MSRP

NVIDIANVIDIA H200 PCIe 141GBBestes Preis-Leistungs-Verhältnis
141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 921%.196 tok/s Dekodierung

Raises estimated decode speed by about 921%.

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

ca. $30,000 MSRP

NVIDIANVIDIA B200 180GBNVIDIA-Upgrade
180 GB VRAM (+52)8000 GB/s (+7727)
C
Raises estimated decode speed by about 921%.196 tok/s Dekodierung

Raises estimated decode speed by about 921%.

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

ca. $30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Yes, NVIDIA DGX Spark 128GB can run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV at F16 quantization (Runs well). The recommended Q4_K_M requires 11.4 GB which exceeds available memory, but at F16 it needs only 44.6 GB. Expected decode speed: 8.0 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 11.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 44.6 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 44.6 GB.

What speed will GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24230ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV on NVIDIA DGX Spark 128GB receives a F grade with 3.5 tok/s and 4K context.

What context window can GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV can safely use up to 642K tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GBSee all hardware for GGUF SOLARized GraniStral 14B 2102 YeAM HCT 32QKV
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