Can Solar Open 100B i1 run on Gaudi 3 128GB?

YES — Runs Great

C54Usable

Estimated from fit model

Solar Open 100B i1 needs ~86.4 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 86.4 GB, 42.5 tok/s, Runs well

86.4 GB required128.0 GB available

68% VRAM used

Fit status

Runs well

Decode

42.5 tok/s

TTFT

4560 ms

Safe context

73K

Memory

86.4 GB / 128.0 GB

Memory breakdown

Weights61.0 GB

KV Cache11.7 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsSolar Open 100B i1 on Gaudi 3 128GB
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: 42.5 tok/s decode · 4.6s TTFT (warm) · 106 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	42.5 tok/s	2487 ms	73K
Coding	C	Runs well	42.5 tok/s	4560 ms	73K
Agentic Coding	C	Runs well	42.5 tok/s	6632 ms	73K
Reasoning	C	Runs well	42.5 tok/s	5389 ms	73K
RAG	C	Runs well	42.5 tok/s	8290 ms	73K

Quantization options

How Solar Open 100B i1 (100B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	39.0 GB	Low	C42
Q3_K_S	3	49.0 GB	Low	C44
NVFP4	4	56.0 GB	Medium	C45
Q4_K_M	4	61.0 GB	Medium	C46
Q5_K_M	5	72.0 GB	High	C48
Q6_K	6	82.0 GB	High	C48
Q8_0Best for your GPU	8	107.0 GB	Very High	C48
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 H200 141GBOpção econômica

141 GB VRAM (+13)4800 GB/s (+1100)

Raises estimated decode speed by about 56%.66.1 tok/s decodificação

Raises estimated decode speed by about 56%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMelhor custo-benefício

141 GB VRAM (+13)4800 GB/s (+1100)

Raises estimated decode speed by about 56%.66.1 tok/s decodificação

Raises estimated decode speed by about 56%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$30,000 MSRP

Frequently asked questions

Can Gaudi 3 128GB run Solar Open 100B i1?

Yes, Gaudi 3 128GB can run Solar Open 100B i1 with a C grade (Runs well). Expected decode speed: 42.5 tok/s.

How much VRAM does Solar Open 100B i1 need?

Solar Open 100B i1 (100B parameters) requires approximately 86.4 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 Gaudi 3 128GB?

On Gaudi 3 128GB, Solar Open 100B i1 achieves approximately 42.5 tokens per second decode speed with a time-to-first-token of 4560ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Solar Open 100B i1 for coding?

For coding workloads, Solar Open 100B i1 on Gaudi 3 128GB receives a C grade with 42.5 tok/s and 73K context.

What context window can Solar Open 100B i1 use on Gaudi 3 128GB?

On Gaudi 3 128GB, Solar Open 100B i1 can safely use up to 73K 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 Gaudi 3 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Gaudi 3 128GB for Solar Open 100B i1?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Gaudi 3 128GB See all hardware for Solar Open 100B i1

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