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URL: https://willitrunai.com/can-run/hf-aaryank--solar-open-100b-gguf-on-a16-64gb

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Can Solar Open 100B run on NVIDIA A16 64GB?

YES — With NVFP4

D28Poor
○Estimated from fit model

Solar Open 100B needs ~75.3 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With NVFP4 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
Share:

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 at Q4_K_M needs 80.3 GB — too much for NVIDIA A16 64GB (64.0 GB). Runs at NVFP4 (75.3 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 80.3 GB, exceeds 64.0 GB available
80.3 GB required64.0 GB available
125% VRAM needed

16.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.6 tok/s

TTFT

54270 ms

Safe context

4K

Memory

80.3 GB / 64.0 GB

Offload

20%

Memory breakdown

Weights61.0 GB
KV Cache11.7 GB
Runtime1.2 GB
Headroom6.4 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsSolar Open 100B on NVIDIA A16 64GB
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: 3.6 tok/s decode · 54.3s TTFT (warm) · 9 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 8.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~8.6 GB host RAM)4.2 tok/s25239 ms4K
CodingFToo heavy3.6 tok/s54270 ms4K
Agentic CodingFToo heavy2.7 tok/s105146 ms4K
ReasoningFToo heavy3.6 tok/s64137 ms4K
RAGFToo heavy2.7 tok/s131432 ms4K

Quantization options

How Solar Open 100B (100B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
39.0 GB
LowC48
Q3_K_SBest for your GPU
3
49.0 GB
LowC48

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

Upgrade options

Hardware that runs Solar Open 100B well

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick
96 GB VRAM (+32)1792 GB/s (+1192)
C
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

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value
96 GB VRAM (+32)1597 GB/s (+997)
C
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
NVIDIA H20 96GBNVIDIA upgrade
96 GB VRAM (+32)4000 GB/s (+3400)
C
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

See all results for NVIDIA A16 64GBSee all hardware for Solar Open 100B
NVFP4
4
56.0 GB
Medium
F0
Q4_K_M
4
61.0 GB
MediumF0
Q5_K_M
5
72.0 GB
HighF0
Q6_K
6
82.0 GB
HighF0
Q8_0
8
107.0 GB
Very HighF0
F16
16
205.0 GB
MaximumF0

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.