Can solar finalised finetuned Model 10.7B i1 run on RTX 2080 Ti 11GB?

YES — Tight Fit

C52Usable

Estimated from fit model

solar finalised finetuned Model 10.7B i1 needs ~10.1 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~61 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 10.1 GB, 61.3 tok/s, Tight fit

10.1 GB required11.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

61.3 tok/s

TTFT

3156 ms

Safe context

28K

Memory

10.1 GB / 11.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelssolar finalised finetuned Model 10.7B i1 on RTX 2080 Ti 11GB
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: 61.3 tok/s decode · 3.2s TTFT (warm) · 153 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	61.3 tok/s	1721 ms	28K
Coding	C	Tight fit	61.3 tok/s	3156 ms	28K
Agentic Coding	C	Runs with offload (needs ~0.2 GB host RAM)	41.9 tok/s	6722 ms	28K
Reasoning	C	Tight fit	61.3 tok/s	3730 ms	28K
RAG	C	Runs with offload (needs ~0.2 GB host RAM)	41.9 tok/s	8402 ms

Quantization options

How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C52
Q3_K_S	3	5.2 GB	Low	C52
NVFP4	4

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

Upgrade options

Hardware that runs solar finalised finetuned Model 10.7B i1 well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.42.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.32.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+1)672 GB/s (+56)

This setup is broadly balanced for this model.64.9 tok/s decode

~$549 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run solar finalised finetuned Model 10.7B i1?

Yes, RTX 2080 Ti 11GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 61.3 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 10.1 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 2080 Ti 11GB?

On RTX 2080 Ti 11GB, solar finalised finetuned Model 10.7B i1 achieves approximately 61.3 tokens per second decode speed with a time-to-first-token of 3156ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run solar finalised finetuned Model 10.7B i1 for coding?

For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 2080 Ti 11GB receives a C grade with 61.3 tok/s and 28K context.

What context window can solar finalised finetuned Model 10.7B i1 use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, solar finalised finetuned Model 10.7B i1 can safely use up to 28K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2080 Ti 11GB See all hardware for solar finalised finetuned Model 10.7B i1

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