Will It Run AI

Can solar finalised finetuned Model 10.7B i1 run on RTX 3060 12GB?

YES — Tight Fit

C51Usable
Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~10.2 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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) — 10.2 GB, 36.4 tok/s, Tight fit
10.2 GB required12.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

36.4 tok/s

TTFT

5318 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 3060 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: 36.4 tok/s decode · 5.3s TTFT (warm) · 91 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well36.4 tok/s2901 ms39K
CodingCTight fit36.4 tok/s5318 ms39K
Agentic CodingCRuns with offload36.4 tok/s7736 ms39K
ReasoningCTight fit36.4 tok/s6285 ms39K
RAGCRuns with offload36.4 tok/s9670 ms39K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC51
Q3_K_S
3
5.2 GB
LowC52
NVFP4
4
6.0 GB
MediumC52
Q4_K_M
4
6.5 GB
MediumC52
Q5_K_M
5
7.7 GB
HighC51
Q6_KBest for your GPU
6
8.8 GB
HighC51
Q8_0
8
11.4 GB
Very HighF0
F16
16
21.9 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien solar finalised finetuned Model 10.7B i1

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+4)448 GB/s (+88)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.42.6 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.32.2 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$499 MSRP

NVIDIARTX 2000 Ada 16GBMejora NVIDIA
16 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.33.5 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$625 MSRP

Frequently asked questions

Can RTX 3060 12GB run solar finalised finetuned Model 10.7B i1?

Yes, RTX 3060 12GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 36.4 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 3060 12GB?

On RTX 3060 12GB, solar finalised finetuned Model 10.7B i1 achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5318ms using Q4_K_M quantization.

Can RTX 3060 12GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 3060 12GB receives a C grade with 36.4 tok/s and 39K context.

What context window can solar finalised finetuned Model 10.7B i1 use on RTX 3060 12GB?

On RTX 3060 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 3060 12GBSee all hardware for solar finalised finetuned Model 10.7B i1
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