Will It Run AI

Can SOLAR 10.7B Instruct v1.0 uncensored run on NVIDIA V100 32GB?

YES — Runs Great

C50Usable
Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~12.2 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~92 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 12.2 GB, 92.4 tok/s, Runs well
12.2 GB required32.0 GB available
38% VRAM used

Fit status

Runs well

Decode

92.4 tok/s

TTFT

2096 ms

Safe context

269K

Memory

12.2 GB / 32.0 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime1.2 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored on NVIDIA V100 32GB
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: 92.4 tok/s decode · 2.1s TTFT (warm) · 231 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 well92.4 tok/s1143 ms269K
CodingCRuns well92.4 tok/s2096 ms269K
Agentic CodingCRuns well92.4 tok/s3048 ms269K
ReasoningCRuns well92.4 tok/s2477 ms269K
RAGCRuns well92.4 tok/s3810 ms269K

Quantization options

How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowC43
Q3_K_S
3
5.2 GB
LowC44
NVFP4
4
6.0 GB
MediumC44
Q4_K_M
4
6.5 GB
MediumC44
Q5_K_M
5
7.7 GB
HighC45
Q6_K
6
8.8 GB
HighC45
Q8_0
8
11.4 GB
Very HighC46
F16Best for your GPU
16
21.9 GB
MaximumC49

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

Frequently asked questions

Can NVIDIA V100 32GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, NVIDIA V100 32GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 92.4 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 12.2 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 V100 32GB?

On NVIDIA V100 32GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 92.4 tokens per second decode speed with a time-to-first-token of 2096ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on NVIDIA V100 32GB receives a C grade with 92.4 tok/s and 269K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 269K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GBSee all hardware for SOLAR 10.7B Instruct v1.0 uncensored
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