Can solar finalised finetuned Model 10.7B i1 run on RTX 2000 Ada 16GB?

YES — Runs Great

C52Usable

○Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~10.6 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 10.6 GB, 33.5 tok/s, Runs well

10.6 GB required16.0 GB available

66% VRAM used

Fit status

Runs well

Decode

33.5 tok/s

TTFT

5773 ms

Safe context

85K

Memory

10.6 GB / 16.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RTX 2000 Ada 16GB
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: 33.5 tok/s decode · 5.8s TTFT (warm) · 84 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	33.5 tok/s	3149 ms	85K
Coding	C	Runs well	33.5 tok/s	5773 ms	85K
Agentic Coding	C	Runs well	33.5 tok/s	8397 ms	85K
Reasoning	C	Runs well	33.5 tok/s	6823 ms	85K
RAG	C	Runs well	33.5 tok/s	10497 ms	85K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 2000 Ada 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C48
Q3_K_S	3	5.2 GB	Low	C49
NVFP4	4	6.0 GB	Medium	C49
Q4_K_M	4	6.5 GB	Medium	C50
Q5_K_M	5	7.7 GB	High	C51
Q6_K	6	8.8 GB	High	C51
Q8_0Best for your GPU	8	11.4 GB	Very High	C50
F16	16	21.9 GB	Maximum	F0

Get started

Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.

Run

lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start

Upgrade options

Hardware that runs solar finalised finetuned Model 10.7B i1 well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 119%.73.5 tok/s decode

Raises estimated decode speed by about 119%.

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

~$899 MSRP

👁 NVIDIA
RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+352)

Raises estimated decode speed by about 128%.76.5 tok/s decode

Raises estimated decode speed by about 128%.