Will It Run AI

Can GGUF SOLARized GraniStral 14B 1902 YeAM HCT run on RTX 2070 Super 8GB?

YES — With Q2_K

D39Poor
Estimated from fit model

GGUF SOLARized GraniStral 14B 1902 YeAM HCT needs ~9.1 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q2_K quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload
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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.

GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q4_K_M needs 12.2 GB — too much for RTX 2070 Super 8GB (8.0 GB). Runs at Q2_K (9.1 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.2 GB, exceeds 8.0 GB available
12.2 GB required8.0 GB available
153% VRAM needed

4.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.3 tok/s

TTFT

20880 ms

Safe context

4K

Memory

12.2 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsGGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 2070 Super 8GB
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: 9.3 tok/s decode · 20.9s TTFT (warm) · 23 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy10.8 tok/s9772 ms4K
CodingFToo heavy9.3 tok/s20880 ms4K
Agentic CodingFToo heavy7.0 tok/s40084 ms4K
ReasoningFToo heavy9.3 tok/s24676 ms4K
RAGFToo heavy7.0 tok/s50104 ms4K

Quantization options

How GGUF SOLARized GraniStral 14B 1902 YeAM HCT (14B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowF0
Q3_K_S
3
6.9 GB
LowF0
NVFP4
4
7.8 GB
MediumF0
Q4_K_M
4
8.5 GB
MediumF0
Q5_K_M
5
10.1 GB
HighF0
Q6_K
6
11.5 GB
HighF0
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

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

升级选项

能流畅运行 GGUF SOLARized GraniStral 14B 1902 YeAM HCT 的硬件

NVIDIARTX 3060 12GB经济之选
12 GB VRAM (+4)
C
Makes the model fit on the accelerator instead of staying completely out of reach.18.9 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

NVIDIARTX 5060 Ti 16GB最佳性价比
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.32.5 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBNVIDIA 升级
16 GB VRAM (+8)
C
Makes the model fit on the accelerator instead of staying completely out of reach.24.6 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can RTX 2070 Super 8GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

Yes, RTX 2070 Super 8GB can run GGUF SOLARized GraniStral 14B 1902 YeAM HCT at Q2_K quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 12.2 GB which exceeds available memory, but at Q2_K it needs only 9.1 GB. Expected decode speed: 23.4 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 12.2 GB at Q4_K_M quantization. On RTX 2070 Super 8GB, it fits at Q2_K using 9.1 GB.

What is the best quantization for GGUF SOLARized GraniStral 14B 1902 YeAM HCT?

The recommended quantization is Q4_K_M, but on RTX 2070 Super 8GB the best fitting quantization is Q2_K, which uses 9.1 GB.

What speed will GGUF SOLARized GraniStral 14B 1902 YeAM HCT run at on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT achieves approximately 23.4 tokens per second decode speed with a time-to-first-token of 8280ms using Q2_K quantization.

Can RTX 2070 Super 8GB run GGUF SOLARized GraniStral 14B 1902 YeAM HCT for coding?

For coding workloads, GGUF SOLARized GraniStral 14B 1902 YeAM HCT on RTX 2070 Super 8GB receives a F grade with 9.3 tok/s and 4K context.

What context window can GGUF SOLARized GraniStral 14B 1902 YeAM HCT use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, GGUF SOLARized GraniStral 14B 1902 YeAM HCT can safely use up to 5K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if GGUF SOLARized GraniStral 14B 1902 YeAM HCT feels slow on RTX 2070 Super 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 2070 Super 8GBSee all hardware for GGUF SOLARized GraniStral 14B 1902 YeAM HCT
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