Will It Run AI

Can SOLAR 10.7B Instruct v1.0 uncensored run on NVIDIA H100 80GB?

YES — Runs Great

C46Usable
Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~17.0 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~150 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) — 17.0 GB, 149.8 tok/s, Runs well
17.0 GB required80.0 GB available
21% VRAM used

Fit status

Runs well

Decode

149.8 tok/s

TTFT

1292 ms

Safe context

820K

Memory

17.0 GB / 80.0 GB

Memory breakdown

Weights6.5 GB
KV Cache1.3 GB
Runtime1.2 GB
Headroom8.0 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored on NVIDIA H100 80GB
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: 149.8 tok/s decode · 1.3s TTFT (warm) · 375 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 well149.8 tok/s705 ms820K
CodingCRuns well149.8 tok/s1292 ms820K
Agentic CodingCRuns well149.8 tok/s1880 ms820K
ReasoningCRuns well149.8 tok/s1527 ms820K
RAGCRuns well149.8 tok/s2350 ms820K

Quantization options

How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.2 GB
LowD40
Q3_K_S
3
5.2 GB
LowD40
NVFP4
4
6.0 GB
MediumD40
Q4_K_M
4
6.5 GB
MediumD40
Q5_K_M
5
7.7 GB
HighD40
Q6_K
6
8.8 GB
HighD40
Q8_0
8
11.4 GB
Very HighC40
F16Best for your GPU
16
21.9 GB
MaximumC42

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 H100 80GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, NVIDIA H100 80GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 149.8 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 17.0 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 H100 80GB?

On NVIDIA H100 80GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 149.8 tokens per second decode speed with a time-to-first-token of 1292ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on NVIDIA H100 80GB receives a C grade with 149.8 tok/s and 820K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 820K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GBSee all hardware for SOLAR 10.7B Instruct v1.0 uncensored
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