Can SOLAR 10.7B Instruct v1.0 uncensored run on NVIDIA A100 80GB?

YES — Runs Great

C46Usable

Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~17.0 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~150 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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 A100 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	149.8 tok/s	705 ms	820K
Coding	C	Runs well	149.8 tok/s	1292 ms	820K
Agentic Coding	C	Runs well	149.8 tok/s	1880 ms	820K
Reasoning	C	Runs well	149.8 tok/s	1527 ms	820K
RAG	C	Runs well	149.8 tok/s	2350 ms	820K

Inference speed

SOLAR 10.7B Instruct v1.0 uncensored inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for SOLAR 10.7B Instruct v1.0 uncensored at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~150 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	149.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	117.4	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	105.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	100.4	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	93.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	85.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	71.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	67.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	60.8	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	46.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	46.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	36.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	36.4	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	33.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	28.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	16.8	Heavy offload

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	D40
Q3_K_S	3	5.2 GB	Low	D40
NVFP4	4	6.0 GB	Medium	D40
Q4_K_M	4	6.5 GB	Medium	D40
Q5_K_M	5	7.7 GB	High	D40
Q6_K	6	8.8 GB	High	D40
Q8_0	8	11.4 GB	Very High	C40
F16Best for your GPU	16	21.9 GB	Maximum	C42

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 A100 80GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, NVIDIA A100 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 A100 80GB?

On NVIDIA A100 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 A100 80GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on NVIDIA A100 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 A100 80GB?

On NVIDIA A100 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 A100 80GB See all hardware for SOLAR 10.7B Instruct v1.0 uncensored

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