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URL: https://willitrunai.com/can-run/hf-mradermacher--solar-open-100b-i1-gguf-on-h200-141gb

⇱ Solar Open 100B i1 on NVIDIA H200 141GB? YES

Can Solar Open 100B i1 run on NVIDIA H200 141GB?

YES — Runs Great

C54Usable
○Estimated from fit model

Solar Open 100B i1 needs ~88.0 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~66 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) — 88.0 GB, 66.1 tok/s, Runs well
88.0 GB required141.0 GB available
62% VRAM used

Fit status

Runs well

Decode

66.1 tok/s

TTFT

2929 ms

Safe context

88K

Memory

88.0 GB / 141.0 GB

Memory breakdown

Weights61.0 GB
KV Cache11.7 GB
Runtime1.2 GB
Headroom14.1 GB

See how fast it feels

See how fast it feelsSolar Open 100B i1 on NVIDIA H200 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: 66.1 tok/s decode · 2.9s TTFT (warm) · 165 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 well66.1 tok/s1598 ms88K
CodingCRuns well66.1 tok/s2929 ms88K
Agentic CodingBRuns well66.1 tok/s4260 ms88K
ReasoningCRuns well66.1 tok/s3462 ms88K
RAGBRuns well66.1 tok/s5325 ms88K

Quantization options

How Solar Open 100B i1 (100B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
39.0 GB
LowC42
Q3_K_S
3
49.0 GB
LowC43
NVFP4
4
56.0 GB
MediumC44
Q4_K_M
4
61.0 GB
MediumC45
Q5_K_M
5
72.0 GB
HighC47
Q6_K
6
82.0 GB
HighC48
Q8_0Best for your GPU
8
107.0 GB
Very HighC48
F16
16
205.0 GB
MaximumF0

Get started

Copy-paste commands to run Solar Open 100B i1 on your machine.

Run

lms load hf-mradermacher--solar-open-100b-i1-gguf && lms server start

Frequently asked questions

See all results for NVIDIA H200 141GBSee all hardware for Solar Open 100B i1