Will It Run AI

Can solar finalised finetuned Model 10.7B i1 run on RTX 4070 Super 12GB?

YES — Tight Fit

C52Usable
Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~9.9 GB VRAM. RTX 4070 Super 12GB has 12.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 9.9 GB, 62.4 tok/s, Tight fit
9.9 GB required12.0 GB available
83% VRAM used

Fit status

Tight fit

Decode

62.4 tok/s

TTFT

3101 ms

Safe context

43K

Memory

9.9 GB / 12.0 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RTX 4070 Super 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: 62.4 tok/s decode · 3.1s TTFT (warm) · 156 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
ChatBRuns well62.4 tok/s1691 ms43K
CodingCTight fit62.4 tok/s3101 ms43K
Agentic CodingCTight fit62.4 tok/s4511 ms43K
ReasoningCTight fit62.4 tok/s3665 ms43K
RAGCTight fit62.4 tok/s5638 ms43K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 4070 Super 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)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.41.3 tok/s decodificación

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

~$449 MSRP

NVIDIARTX 5070 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+4)896 GB/s (+392)
B
Sube la velocidad estimada de decodificación alrededor de un 37%.85.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 37%.

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

~$749 MSRP

NVIDIARTX 4070 Ti Super 16GBMejora NVIDIA
16 GB VRAM (+4)672 GB/s (+168)
B
Sube la velocidad estimada de decodificación alrededor de un 39%.86.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 39%.

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

~$799 MSRP

Frequently asked questions

Can RTX 4070 Super 12GB run solar finalised finetuned Model 10.7B i1?

Yes, RTX 4070 Super 12GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 62.4 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 9.9 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 4070 Super 12GB?

On RTX 4070 Super 12GB, solar finalised finetuned Model 10.7B i1 achieves approximately 62.4 tokens per second decode speed with a time-to-first-token of 3101ms using Q4_K_M quantization.

Can RTX 4070 Super 12GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 4070 Super 12GB receives a C grade with 62.4 tok/s and 43K context.

What context window can solar finalised finetuned Model 10.7B i1 use on RTX 4070 Super 12GB?

On RTX 4070 Super 12GB, solar finalised finetuned Model 10.7B i1 can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4070 Super 12GBSee all hardware for solar finalised finetuned Model 10.7B i1
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