Will It Run AI

Can Solar Open 100B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C53Usable
Estimated from fit model

Solar Open 100B needs ~86.4 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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, 33.0 tok/s, Runs well
86.4 GB required128.0 GB available
68% VRAM used

Fit status

Runs well

Decode

33.0 tok/s

TTFT

5858 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 on Intel Data Center GPU Max 1550 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: 33.0 tok/s decode · 5.9s TTFT (warm) · 83 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well33.0 tok/s3195 ms73K
CodingCRuns well33.0 tok/s5858 ms73K
Agentic CodingCRuns well33.0 tok/s8521 ms73K
ReasoningCRuns well33.0 tok/s6923 ms73K
RAGCRuns well33.0 tok/s10651 ms73K

Quantization options

How Solar Open 100B (100B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
39.0 GB
LowC42
Q3_K_S
3
49.0 GB
LowC44
NVFP4
4
56.0 GB
MediumC45
Q4_K_M
4
61.0 GB
MediumC46
Q5_K_M
5
72.0 GB
HighC48
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 on your machine.

Run

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

Opções de upgrade

Hardware que roda bem Solar Open 100B

NVIDIANVIDIA H200 141GBOpção econômica
141 GB VRAM (+13)4800 GB/s (+1600)
C
Raises estimated decode speed by about 100%.66.1 tok/s decodificação

Raises estimated decode speed by about 100%.

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

NVIDIANVIDIA H200 PCIe 141GBMelhor custo-benefício
141 GB VRAM (+13)4800 GB/s (+1600)
C
Raises estimated decode speed by about 100%.66.1 tok/s decodificação

Raises estimated decode speed by about 100%.

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 Intel Data Center GPU Max 1550 128GB run Solar Open 100B?

Yes, Intel Data Center GPU Max 1550 128GB can run Solar Open 100B with a C grade (Runs well). Expected decode speed: 33.0 tok/s.

How much VRAM does Solar Open 100B need?

Solar Open 100B (100B parameters) requires approximately 86.4 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 Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Solar Open 100B achieves approximately 33.0 tokens per second decode speed with a time-to-first-token of 5858ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Solar Open 100B for coding?

For coding workloads, Solar Open 100B on Intel Data Center GPU Max 1550 128GB receives a C grade with 33.0 tok/s and 73K context.

What context window can Solar Open 100B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Solar Open 100B 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 feels slow on Intel Data Center GPU Max 1550 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 Intel Data Center GPU Max 1550 128GB for Solar Open 100B?

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 Intel Data Center GPU Max 1550 128GBSee all hardware for Solar Open 100B
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