Can solar finalised finetuned Model 10.7B i1 run on RTX 5000 Ada 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~12.2 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~71 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 12.2 GB, 70.6 tok/s, Runs well

12.2 GB required32.0 GB available

38% VRAM used

Fit status

Runs well

Decode

70.6 tok/s

TTFT

2742 ms

Safe context

269K

Memory

12.2 GB / 32.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RTX 5000 Ada 32GB
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: 70.6 tok/s decode · 2.7s TTFT (warm) · 177 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	70.6 tok/s	1496 ms	269K
Coding	C	Runs well	70.6 tok/s	2742 ms	269K
Agentic Coding	C	Runs well	70.6 tok/s	3989 ms	269K
Reasoning	C	Runs well	70.6 tok/s	3241 ms	269K
RAG	C	Runs well	70.6 tok/s	4986 ms	269K

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C43
Q3_K_S	3	5.2 GB	Low	C43
NVFP4	4	6.0 GB	Medium	C44
Q4_K_M	4	6.5 GB	Medium	C44
Q5_K_M	5	7.7 GB	High	C44
Q6_K	6	8.8 GB	High	C45
Q8_0	8	11.4 GB	Very High	C46
F16Best for your GPU	16	21.9 GB	Maximum	C49

Get started

Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.

Run

lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start

Frequently asked questions

Can RTX 5000 Ada 32GB run solar finalised finetuned Model 10.7B i1?

Yes, RTX 5000 Ada 32GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 70.6 tok/s.

How much VRAM does solar finalised finetuned Model 10.7B i1 need?

solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 12.2 GB of memory with Q4_K_M quantization.

What is the best quantization for solar finalised finetuned Model 10.7B i1?

The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will solar finalised finetuned Model 10.7B i1 run at on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, solar finalised finetuned Model 10.7B i1 achieves approximately 70.6 tokens per second decode speed with a time-to-first-token of 2742ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 5000 Ada 32GB receives a C grade with 70.6 tok/s and 269K context.

What context window can solar finalised finetuned Model 10.7B i1 use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, solar finalised finetuned Model 10.7B i1 can safely use up to 269K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for solar finalised finetuned Model 10.7B i1

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