Can Solar Open 69B REAP i1 run on NVIDIA A100 80GB?

YES — Runs Great

C54Usable

○Estimated from fit model

Solar Open 69B REAP i1 needs ~59.4 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~41 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) — 59.4 GB, 40.7 tok/s, Runs well

59.4 GB required80.0 GB available

74% VRAM used

Fit status

Runs well

Decode

40.7 tok/s

TTFT

4758 ms

Safe context

57K

Memory

59.4 GB / 80.0 GB

Memory breakdown

Weights42.1 GB

KV Cache8.1 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsSolar Open 69B REAP i1 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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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	40.7 tok/s	2595 ms	57K
Coding	C	Runs well	40.7 tok/s	4758 ms	57K
Agentic Coding	C	Tight fit	40.7 tok/s	6920 ms	57K
Reasoning	C	Runs well	40.7 tok/s	5623 ms	57K
RAG	C	Tight fit	40.7 tok/s	8650 ms	57K

Quantization options

How Solar Open 69B REAP i1 (69B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.9 GB	Low	C43
Q3_K_S	3	33.8 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run Solar Open 69B REAP i1 on your machine.

Run

lms load hf-mradermacher--solar-open-69b-reap-i1-gguf && lms server start

Upgrade options

Hardware that runs Solar Open 69B REAP i1 well

👁 NVIDIA
NVIDIA H20 96GBBudget pick

96 GB VRAM (+16)4000 GB/s (+1961)

Raises estimated decode speed by about 89%.77 tok/s decode

Raises estimated decode speed by about 89%.

~$12,000 MSRP

👁 NVIDIA
NVIDIA GH200 96GBBest value