Can Solar Open 100B run on NVIDIA H100 PCIe 80GB?

YES — With Offload

C49Usable

Estimated from fit model

Solar Open 100B needs ~81.6 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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) — 81.6 GB, 20.2 tok/s, Runs with offload (needs ~1.2 GB host RAM)

81.6 GB required80.0 GB available

102% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~1.2 GB host RAM)

Decode

20.2 tok/s

TTFT

9576 ms

Safe context

14K

Memory

81.6 GB / 80.0 GB

Memory breakdown

Weights61.0 GB

KV Cache11.7 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsSolar Open 100B on NVIDIA H100 PCIe 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: 20.2 tok/s decode · 9.6s TTFT (warm) · 51 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	27.5 tok/s	3834 ms	14K
Coding	C	Runs with offload (needs ~1.2 GB host RAM)	20.2 tok/s	9576 ms	14K
Agentic Coding	D	Very compromised (needs ~8.7 GB host RAM)	15.4 tok/s	18313 ms	14K
Reasoning	C	Runs with offload (needs ~1.2 GB host RAM)	20.2 tok/s	11317 ms	14K
RAG	D	Very compromised (needs ~8.7 GB host RAM)	15.4 tok/s	22892 ms	14K

Quantization options

How Solar Open 100B (100B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	39.0 GB	Low	C47
Q3_K_S	3	49.0 GB	Low	C48
NVFP4	4	56.0 GB	Medium	C48
Q4_K_MBest for your GPU	4	61.0 GB	Medium	C48
Q5_K_M	5	72.0 GB	High	F0
Q6_K	6	82.0 GB	High	F0
Q8_0	8	107.0 GB	Very High	F0
F16	16	205.0 GB	Maximum	F0

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

アップグレードオプション

Solar Open 100Bを快適に動かすハードウェア

RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視

96 GB VRAM (+16)

This setup is broadly balanced for this model.24.7 tok/sデコード

〜$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良

96 GB VRAM (+16)

This setup is broadly balanced for this model.22 tok/sデコード

〜$9,999 MSRP

NVIDIA H20 96GBNVIDIAアップグレード

96 GB VRAM (+16)4000 GB/s (+2000)

Raises estimated decode speed by about 163%.53.1 tok/sデコード

Raises estimated decode speed by about 163%.

〜$12,000 MSRP

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Solar Open 100B?

Yes, NVIDIA H100 PCIe 80GB can run Solar Open 100B with a C grade (Runs with offload (needs ~1.2 GB host RAM)). Expected decode speed: 20.2 tok/s.

How much VRAM does Solar Open 100B need?

Solar Open 100B (100B parameters) requires approximately 81.6 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 H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Solar Open 100B achieves approximately 20.2 tokens per second decode speed with a time-to-first-token of 9576ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Solar Open 100B for coding?

For coding workloads, Solar Open 100B on NVIDIA H100 PCIe 80GB receives a C grade with 20.2 tok/s and 14K context.

What context window can Solar Open 100B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Solar Open 100B can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Solar Open 100B feels slow on NVIDIA H100 PCIe 80GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Solar Open 100B

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