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URL: https://willitrunai.com/can-run/hf-mradermacher--solar-open-69b-reap-i1-gguf-on-h200-pcie-141gb

⇱ Solar Open 69B REAP i1 on NVIDIA H200 PCIe 141GB? YES

Can Solar Open 69B REAP i1 run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

C52Usable
○Estimated from fit model

Solar Open 69B REAP i1 needs ~65.5 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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) — 65.5 GB, 95.8 tok/s, Runs well
65.5 GB required141.0 GB available
46% VRAM used

Fit status

Runs well

Decode

95.8 tok/s

TTFT

2021 ms

Safe context

165K

Memory

65.5 GB / 141.0 GB

Memory breakdown

Weights42.1 GB
KV Cache8.1 GB
Runtime1.2 GB
Headroom14.1 GB

See how fast it feels

See how fast it feelsSolar Open 69B REAP i1 on NVIDIA H200 PCIe 141GB
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: 95.8 tok/s decode · 2.0s TTFT (warm) · 240 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well95.8 tok/s1102 ms165K
CodingCRuns well95.8 tok/s2021 ms165K
Agentic CodingCRuns well95.8 tok/s2940 ms165K
ReasoningCRuns well95.8 tok/s2388 ms165K
RAGCRuns well95.8 tok/s3675 ms165K

Quantization options

How Solar Open 69B REAP i1 (69B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
26.9 GB
LowD40
Q3_K_S
3
33.8 GB
LowC41
NVFP4
4
38.6 GB
MediumC41
Q4_K_M
4
42.1 GB
MediumC42
Q5_K_M
5
49.7 GB
HighC43
Q6_K
6
56.6 GB
HighC44
Q8_0Best for your GPU
8
73.8 GB
Very HighC47
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

Frequently asked questions

See all results for NVIDIA H200 PCIe 141GBSee all hardware for Solar Open 69B REAP i1