Will It Run AI

Can SOLAR 10.7B Instruct v1.0 uncensored run on RTX 4080 Laptop 12GB?

YES — Tight Fit

C52Usable
Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~9.9 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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, 54.2 tok/s, Tight fit
9.9 GB required12.0 GB available
83% VRAM used

Fit status

Tight fit

Decode

54.2 tok/s

TTFT

3572 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 10.7B Instruct v1.0 uncensored on RTX 4080 Laptop 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: 54.2 tok/s decode · 3.6s TTFT (warm) · 136 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 well54.2 tok/s1948 ms43K
CodingCTight fit54.2 tok/s3572 ms43K
Agentic CodingCTight fit54.2 tok/s5195 ms43K
ReasoningCTight fit54.2 tok/s4221 ms43K
RAGCTight fit54.2 tok/s6494 ms43K

Quantization options

How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on RTX 4080 Laptop 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
HighC52
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 10.7B Instruct v1.0 uncensored on your machine.

Run

lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien SOLAR 10.7B Instruct v1.0 uncensored

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+4)448 GB/s (+16)
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 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.33.8 tok/s decodificación

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

~$499 MSRP

NVIDIARTX 5070 Ti 16GBMejora NVIDIA
16 GB VRAM (+4)896 GB/s (+464)
B
Sube la velocidad estimada de decodificación alrededor de un 57%.85.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 57%.

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

~$749 MSRP

Frequently asked questions

Can RTX 4080 Laptop 12GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, RTX 4080 Laptop 12GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Tight fit). Expected decode speed: 54.2 tok/s.

How much VRAM does SOLAR 10.7B Instruct v1.0 uncensored need?

SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B Instruct v1.0 uncensored?

The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B Instruct v1.0 uncensored run at on RTX 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 54.2 tokens per second decode speed with a time-to-first-token of 3572ms using Q4_K_M quantization.

Can RTX 4080 Laptop 12GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on RTX 4080 Laptop 12GB receives a C grade with 54.2 tok/s and 43K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on RTX 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, SOLAR 10.7B Instruct v1.0 uncensored 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 4080 Laptop 12GBSee all hardware for SOLAR 10.7B Instruct v1.0 uncensored
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