Can Solar Open 100B i1 run on NVIDIA L40 48GB?

Q: What should I upgrade first if Solar Open 100B i1 feels slow on NVIDIA L40 48GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

D30Poor

Estimated from fit model

Solar Open 100B i1 needs ~56.7 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With Q2_K quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload

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.

Solar Open 100B i1 at Q4_K_M needs 78.7 GB — too much for NVIDIA L40 48GB (48.0 GB). Runs at Q2_K (56.7 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 78.7 GB, exceeds 48.0 GB available

78.7 GB required48.0 GB available

164% VRAM needed

30.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.9 tok/s

TTFT

66191 ms

Safe context

Memory

78.7 GB / 48.0 GB

Offload

40%

Memory breakdown

Weights61.0 GB

KV Cache11.7 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsSolar Open 100B i1 on NVIDIA L40 48GB
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: 2.9 tok/s decode · 66.2s TTFT (warm) · 7 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 6.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.4 tok/s	30679 ms	4K
Coding	F	Too heavy	2.9 tok/s	66191 ms	4K
Agentic Coding	F	Too heavy	2.2 tok/s	128942 ms	4K
Reasoning	F	Too heavy	2.9 tok/s	78225 ms	4K
RAG	F	Too heavy	2.2 tok/s	161177 ms	4K

Quantization options

How Solar Open 100B i1 (100B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	39.0 GB	Low	F0
Q3_K_S	3	49.0 GB	Low	F0
NVFP4	4

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

Upgrade options

Hardware that runs Solar Open 100B i1 well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+48)1792 GB/s (+928)

Makes the model fit on the accelerator instead of staying completely out of reach.24.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+48)1597 GB/s (+733)

Makes the model fit on the accelerator instead of staying completely out of reach.22 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+48)4000 GB/s (+3136)

Makes the model fit on the accelerator instead of staying completely out of reach.53.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$12,000 MSRP

Frequently asked questions

Can NVIDIA L40 48GB run Solar Open 100B i1?

Yes, NVIDIA L40 48GB can run Solar Open 100B i1 at Q2_K quantization (Very compromised (needs ~6 GB host RAM)). The recommended Q4_K_M requires 78.7 GB which exceeds available memory, but at Q2_K it needs only 56.7 GB. Expected decode speed: 7.8 tok/s.

How much VRAM does Solar Open 100B i1 need?

Solar Open 100B i1 (100B parameters) requires approximately 78.7 GB at Q4_K_M quantization. On NVIDIA L40 48GB, it fits at Q2_K using 56.7 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA L40 48GB the best fitting quantization is Q2_K, which uses 56.7 GB.

What speed will Solar Open 100B i1 run at on NVIDIA L40 48GB?

On NVIDIA L40 48GB, Solar Open 100B i1 achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24973ms using Q2_K quantization.

Can NVIDIA L40 48GB run Solar Open 100B i1 for coding?

For coding workloads, Solar Open 100B i1 on NVIDIA L40 48GB receives a F grade with 2.9 tok/s and 4K context.

What context window can Solar Open 100B i1 use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, Solar Open 100B i1 can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L40 48GB See all hardware for Solar Open 100B i1

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