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

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

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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	13.8 tok/s	7670 ms	4K
Coding	F	Too heavy	11.7 tok/s	16496 ms	4K
Agentic Coding	F	Too heavy	8.8 tok/s	31966 ms	4K
Reasoning	F	Too heavy	11.7 tok/s	19495 ms	4K
RAG	F	Too heavy	8.8 tok/s	39958 ms	4K

Inference speed

Solar Open 69B REAP i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 69B REAP i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	15.2	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	10.2	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	8.0	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	7.3	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	7.3	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	6.5	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	4.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.1	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.8	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.7	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.5	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

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

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	26.9 GB	Low	C48
Q3_K_S	3	33.8 GB	Low	F0
NVFP4	4	38.6 GB	Medium	F0
Q4_K_M	4	42.1 GB	Medium	F0
Q5_K_M	5	49.7 GB	High	F0
Q6_K	6	56.6 GB	High	F0
Q8_0	8	73.8 GB	Very High	F0
F16	16	141.5 GB	Maximum	F0

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

Opciones de mejora

Hardware que ejecuta bien Solar Open 69B REAP i1

RTX A6000 48GBMejor relación calidad-precio

48 GB VRAM (+8)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.7.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBMejora NVIDIA

48 GB VRAM (+8)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.14.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 27%.

~$4,999 MSRP

NVIDIA A16 64GBOpción económica

64 GB VRAM (+24)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.11.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$6,500 MSRP

NVIDIA H100 80GBMayor salto

80 GB VRAM (+40)3350 GB/s (+1795)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.66.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$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 40GB See all hardware for Solar Open 69B REAP i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-open-69b-reap-i1-gguf-on-a100-40gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: