Can Solar Open 100B i1 run on NVIDIA H100 80GB?

YES — With Offload

C51Usable

Estimated from fit model

Solar Open 100B i1 needs ~81.6 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~38 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, 38.1 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

38.1 tok/s

TTFT

5086 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 i1 on NVIDIA H100 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: 38.1 tok/s decode · 5.1s TTFT (warm) · 95 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	46.1 tok/s	2289 ms	14K
Coding	C	Runs with offload (needs ~1.2 GB host RAM)	38.1 tok/s	5086 ms	14K
Agentic Coding	C	Very compromised (needs ~8.7 GB host RAM)	30.5 tok/s	9230 ms	14K
Reasoning	C	Runs with offload (needs ~1.2 GB host RAM)	38.1 tok/s	6010 ms	14K
RAG	C	Very compromised (needs ~8.7 GB host RAM)	30.5 tok/s	11538 ms	14K

Inference speed

Solar Open 100B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 100B i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	9.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	9.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.6	Tight
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	7.2	Tight
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.9	Too big
RTX 5090 32GB	32 GB	Q4_K_M	3.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.8	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.5	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.2	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	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
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 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 100B i1 (100B params) fits at each quantization level on NVIDIA H100 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 i1 on your machine.

Run

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

Opções de upgrade

Hardware que roda bem Solar Open 100B i1

NVIDIA H20 96GBOpção econômica

96 GB VRAM (+16)4000 GB/s (+650)

Raises estimated decode speed by about 39%.53.1 tok/s decodificação

Raises estimated decode speed by about 39%.

~$12,000 MSRP

NVIDIA H200 141GBMelhor custo-benefício

141 GB VRAM (+61)4800 GB/s (+1450)

Raises estimated decode speed by about 73%.66.1 tok/s decodificação

Raises estimated decode speed by about 73%.

Adds memory headroom for longer context windows and future model growth.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBUpgrade NVIDIA

141 GB VRAM (+61)4800 GB/s (+1450)

Raises estimated decode speed by about 73%.66.1 tok/s decodificação

Raises estimated decode speed by about 73%.

Adds memory headroom for longer context windows and future model growth.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA H100 80GB run Solar Open 100B i1?

Yes, NVIDIA H100 80GB can run Solar Open 100B i1 with a C grade (Runs with offload (needs ~1.2 GB host RAM)). Expected decode speed: 38.1 tok/s.

How much VRAM does Solar Open 100B i1 need?

Solar Open 100B i1 (100B parameters) requires approximately 81.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 100B i1?

The recommended quantization for Solar Open 100B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 100B i1 run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Solar Open 100B i1 achieves approximately 38.1 tokens per second decode speed with a time-to-first-token of 5086ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Solar Open 100B i1 for coding?

For coding workloads, Solar Open 100B i1 on NVIDIA H100 80GB receives a C grade with 38.1 tok/s and 14K context.

What context window can Solar Open 100B i1 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Solar Open 100B i1 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 i1 feels slow on NVIDIA H100 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 80GB See all hardware for Solar Open 100B i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-open-100b-i1-gguf-on-h100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: