Can Solar Open 69B REAP i1 run on NVIDIA H800 80GB?

YES — Runs Great

B55Good

Estimated from fit model

Solar Open 69B REAP i1 needs ~59.4 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~58 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, 57.7 tok/s, Runs well

59.4 GB required80.0 GB available

74% VRAM used

Fit status

Runs well

Decode

57.7 tok/s

TTFT

3353 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 H800 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: 57.7 tok/s decode · 3.4s TTFT (warm) · 144 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	57.7 tok/s	1829 ms	57K
Coding	B	Runs well	57.7 tok/s	3353 ms	57K
Agentic Coding	C	Tight fit	57.7 tok/s	4878 ms	57K
Reasoning	B	Runs well	57.7 tok/s	3963 ms	57K
RAG	C	Tight fit	57.7 tok/s	6097 ms	57K

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 H800 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	38.6 GB	Medium	C46
Q4_K_M	4	42.1 GB	Medium	C47
Q5_K_M	5	49.7 GB	High	C48
Q6_KBest for your GPU	6	56.6 GB	High	C48
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

Frequently asked questions

Can NVIDIA H800 80GB run Solar Open 69B REAP i1?

Yes, NVIDIA H800 80GB can run Solar Open 69B REAP i1 with a B grade (Runs well). Expected decode speed: 57.7 tok/s.

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

Solar Open 69B REAP i1 (69B parameters) requires approximately 59.4 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Solar Open 69B REAP i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 69B REAP i1 run at on NVIDIA H800 80GB?

On NVIDIA H800 80GB, Solar Open 69B REAP i1 achieves approximately 57.7 tokens per second decode speed with a time-to-first-token of 3353ms using Q4_K_M quantization.

Can NVIDIA H800 80GB run Solar Open 69B REAP i1 for coding?

For coding workloads, Solar Open 69B REAP i1 on NVIDIA H800 80GB receives a B grade with 57.7 tok/s and 57K context.

What context window can Solar Open 69B REAP i1 use on NVIDIA H800 80GB?

On NVIDIA H800 80GB, Solar Open 69B REAP i1 can safely use up to 57K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H800 80GB See all hardware for Solar Open 69B REAP i1

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-open-69b-reap-i1-gguf-on-h800-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

57.7 tok/s

1829 ms

57K

Coding

Runs well

57.7 tok/s

3353 ms

57K

Agentic Coding

Tight fit

57.7 tok/s

4878 ms

57K

Reasoning

Runs well

57.7 tok/s

3963 ms

57K

RAG

Tight fit

57.7 tok/s

6097 ms

57K

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

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

Quantization options

How Solar Open 69B REAP i1 (69B params) fits at each quantization level on NVIDIA H800 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	38.6 GB	Medium	C46
Q4_K_M	4	42.1 GB	Medium	C47
Q5_K_M	5	49.7 GB	High	C48
Q6_KBest for your GPU	6	56.6 GB	High	C48
Q8_0	8	73.8 GB	Very High	F0
F16	16	141.5 GB	Maximum	F0

Frequently asked questions

Can NVIDIA H800 80GB run Solar Open 69B REAP i1?

Yes, NVIDIA H800 80GB can run Solar Open 69B REAP i1 with a B grade (Runs well). Expected decode speed: 57.7 tok/s.

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

Solar Open 69B REAP i1 (69B parameters) requires approximately 59.4 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Solar Open 69B REAP i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 69B REAP i1 run at on NVIDIA H800 80GB?

On NVIDIA H800 80GB, Solar Open 69B REAP i1 achieves approximately 57.7 tokens per second decode speed with a time-to-first-token of 3353ms using Q4_K_M quantization.

Can NVIDIA H800 80GB run Solar Open 69B REAP i1 for coding?

For coding workloads, Solar Open 69B REAP i1 on NVIDIA H800 80GB receives a B grade with 57.7 tok/s and 57K context.

What context window can Solar Open 69B REAP i1 use on NVIDIA H800 80GB?

On NVIDIA H800 80GB, Solar Open 69B REAP i1 can safely use up to 57K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.