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

YES — Runs Great

C54Usable

Estimated from fit model

Solar Open 100B 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

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 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	66.1 tok/s	1598 ms	88K
Coding	C	Runs well	66.1 tok/s	2929 ms	88K
Agentic Coding	B	Runs well	66.1 tok/s	4260 ms	88K
Reasoning	C	Runs well	66.1 tok/s	3462 ms	88K
RAG	B	Runs well	66.1 tok/s	5325 ms	88K

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	39.0 GB	Low	C42
Q3_K_S	3	49.0 GB	Low	C43
NVFP4	4

Get started

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

Run

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

Frequently asked questions

Can NVIDIA H200 141GB run Solar Open 100B?

Yes, NVIDIA H200 141GB can run Solar Open 100B with a C grade (Runs well). Expected decode speed: 66.1 tok/s.

How much VRAM does Solar Open 100B need?

Solar Open 100B (100B parameters) requires approximately 88.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 100B?

The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 100B run at on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Solar Open 100B achieves approximately 66.1 tokens per second decode speed with a time-to-first-token of 2929ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Solar Open 100B for coding?

For coding workloads, Solar Open 100B on NVIDIA H200 141GB receives a C grade with 66.1 tok/s and 88K context.

What context window can Solar Open 100B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Solar Open 100B can safely use up to 88K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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

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