Can solar finalised finetuned Model 10.7B i1 run on RTX 3080 Ti 12GB?

YES — Tight Fit

C54Usable

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~10.2 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~103 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: 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) — 10.2 GB, 103.4 tok/s, Tight fit

10.2 GB required12.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

103.4 tok/s

TTFT

1872 ms

Safe context

39K

Memory

10.2 GB / 12.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RTX 3080 Ti 12GB
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: 103.4 tok/s decode · 1.9s TTFT (warm) · 259 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	B	Runs well	103.4 tok/s	1021 ms	39K
Coding	C	Tight fit	103.4 tok/s	1872 ms	39K
Agentic Coding	C	Runs with offload	103.4 tok/s	2723 ms	39K
Reasoning	C	Tight fit	103.4 tok/s	2213 ms	39K
RAG	C	Runs with offload	103.4 tok/s	3404 ms	39K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C51
Q3_K_S	3	5.2 GB	Low	C52
NVFP4	4

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

RTX 5070 Ti 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.87.9 tok/s decode

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

~$749 MSRP

RTX 4070 Ti Super 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.82.4 tok/s decode

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

~$799 MSRP

RTX 4080 Super 16GBNVIDIA upgrade

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.93.6 tok/s decode

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

~$999 MSRP

Frequently asked questions

Can RTX 3080 Ti 12GB run solar finalised finetuned Model 10.7B i1?

Yes, RTX 3080 Ti 12GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 103.4 tok/s.

How much VRAM does solar finalised finetuned Model 10.7B i1 need?

solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.

What is the best quantization for solar finalised finetuned Model 10.7B i1?

The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will solar finalised finetuned Model 10.7B i1 run at on RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, solar finalised finetuned Model 10.7B i1 achieves approximately 103.4 tokens per second decode speed with a time-to-first-token of 1872ms using Q4_K_M quantization.

Can RTX 3080 Ti 12GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 3080 Ti 12GB receives a C grade with 103.4 tok/s and 39K context.

What context window can solar finalised finetuned Model 10.7B i1 use on RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, solar finalised finetuned Model 10.7B i1 can safely use up to 39K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3080 Ti 12GB See all hardware for solar finalised finetuned Model 10.7B i1

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