Can SOLAR 10.7B Instruct v1.0 uncensored run on NVIDIA A2 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~10.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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, 23.9 tok/s, Runs well

10.6 GB required16.0 GB available

66% VRAM used

Fit status

Runs well

Decode

23.9 tok/s

TTFT

8100 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 10.7B Instruct v1.0 uncensored on NVIDIA A2 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: 23.9 tok/s decode · 8.1s TTFT (warm) · 60 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	23.9 tok/s	4418 ms	85K
Coding	C	Runs well	23.9 tok/s	8100 ms	85K
Agentic Coding	C	Runs well	23.9 tok/s	11782 ms	85K
Reasoning	C	Runs well	23.9 tok/s	9573 ms	85K
RAG	C	Runs well	23.9 tok/s	14728 ms	85K

Quantization options

How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on NVIDIA A2 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

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

Upgrade options

Hardware that runs SOLAR 10.7B Instruct v1.0 uncensored well

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 320%.100.4 tok/s decode

Raises estimated decode speed by about 320%.

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

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+8)1008 GB/s (+808)

Raises estimated decode speed by about 391%.117.4 tok/s decode

Raises estimated decode speed by about 391%.

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

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 262%.86.5 tok/s decode

Raises estimated decode speed by about 262%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, NVIDIA A2 16GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 23.9 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 10.6 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 23.9 tokens per second decode speed with a time-to-first-token of 8100ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on NVIDIA A2 16GB receives a C grade with 23.9 tok/s and 85K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 85K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for SOLAR 10.7B Instruct v1.0 uncensored

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