Will It Run AI

Can Solar Open 69B REAP i1 run on NVIDIA A100 40GB?

YES — With Q3_K_S

D33Poor
Estimated from fit model

Solar Open 69B REAP i1 needs ~47.1 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q3_K_S quantization, expect ~19 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload
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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.

Solar Open 69B REAP i1 at Q4_K_M needs 55.4 GB — too much for NVIDIA A100 40GB (40.0 GB). Runs at Q3_K_S (47.1 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 55.4 GB, exceeds 40.0 GB available
55.4 GB required40.0 GB available
139% VRAM needed

15.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.7 tok/s

TTFT

16496 ms

Safe context

4K

Memory

55.4 GB / 40.0 GB

Offload

30%

Memory breakdown

Weights42.1 GB
KV Cache8.1 GB
Runtime1.2 GB
Headroom4.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsSolar Open 69B REAP i1 on NVIDIA A100 40GB
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: 11.7 tok/s decode · 16.5s TTFT (warm) · 29 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 5.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy13.8 tok/s7670 ms4K
CodingFToo heavy11.7 tok/s16496 ms4K
Agentic CodingFToo heavy8.8 tok/s31966 ms4K
ReasoningFToo heavy11.7 tok/s19495 ms4K
RAGFToo heavy8.8 tok/s39958 ms4K

Quantization options

How Solar Open 69B REAP i1 (69B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
26.9 GB
LowC48
Q3_K_S
3
33.8 GB
LowF0
NVFP4
4
38.6 GB
MediumF0
Q4_K_M
4
42.1 GB
MediumF0
Q5_K_M
5
49.7 GB
HighF0
Q6_K
6
56.6 GB
HighF0
Q8_0
8
73.8 GB
Very HighF0
F16
16
141.5 GB
MaximumF0

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

Opções de upgrade

Hardware que roda bem Solar Open 69B REAP i1

NVIDIARTX A6000 48GBMelhor custo-benefício
48 GB VRAM (+8)
D
Makes the model fit on the accelerator instead of staying completely out of reach.7.5 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

~$4,650 MSRP

NVIDIARTX PRO 5000 Blackwell 48GBUpgrade NVIDIA
48 GB VRAM (+8)
D
Makes the model fit on the accelerator instead of staying completely out of reach.14.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 27%.

~$4,999 MSRP

NVIDIANVIDIA A16 64GBOpção econômica
64 GB VRAM (+24)
C
Makes the model fit on the accelerator instead of staying completely out of reach.11.1 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$6,500 MSRP

NVIDIANVIDIA H100 80GBMaior salto
80 GB VRAM (+40)3350 GB/s (+1795)
B
Makes the model fit on the accelerator instead of staying completely out of reach.66.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$40,000 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run Solar Open 69B REAP i1?

Yes, NVIDIA A100 40GB can run Solar Open 69B REAP i1 at Q3_K_S quantization (Very compromised (needs ~5.1 GB host RAM)). The recommended Q4_K_M requires 55.4 GB which exceeds available memory, but at Q3_K_S it needs only 47.1 GB. Expected decode speed: 19.1 tok/s.

How much VRAM does Solar Open 69B REAP i1 need?

Solar Open 69B REAP i1 (69B parameters) requires approximately 55.4 GB at Q4_K_M quantization. On NVIDIA A100 40GB, it fits at Q3_K_S using 47.1 GB.

What is the best quantization for Solar Open 69B REAP i1?

The recommended quantization is Q4_K_M, but on NVIDIA A100 40GB the best fitting quantization is Q3_K_S, which uses 47.1 GB.

What speed will Solar Open 69B REAP i1 run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Solar Open 69B REAP i1 achieves approximately 19.1 tokens per second decode speed with a time-to-first-token of 10132ms using Q3_K_S quantization.

Can NVIDIA A100 40GB run Solar Open 69B REAP i1 for coding?

For coding workloads, Solar Open 69B REAP i1 on NVIDIA A100 40GB receives a F grade with 11.7 tok/s and 4K context.

What context window can Solar Open 69B REAP i1 use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Solar Open 69B REAP i1 can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Solar Open 69B REAP i1 feels slow on NVIDIA A100 40GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for NVIDIA A100 40GBSee all hardware for Solar Open 69B REAP i1
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