Can Dolphin 2.9 8B run on RTX 3080 12GB?

YES — Runs Great

B58Good

Estimated from fit model

Dolphin 2.9 8B needs ~9.2 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 9.2 GB, 112.0 tok/s, Runs well

9.2 GB required12.0 GB available

77% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

33K

Memory

9.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsDolphin 2.9 8B on RTX 3080 12GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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	B	Runs well	112.0 tok/s	943 ms	33K
Coding	B	Runs well	112.0 tok/s	1729 ms	33K
Agentic Coding	C	Tight fit	112.0 tok/s	2514 ms	33K
Reasoning	B	Runs well	112.0 tok/s	2043 ms	33K
RAG	C	Tight fit	112.0 tok/s	3143 ms	33K

Quantization options

How Dolphin 2.9 8B (8B params) fits at each quantization level on RTX 3080 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C50
Q3_K_S	3	3.9 GB	Low	C51
NVFP4	4	4.5 GB	Medium	C52
Q4_K_M	4	4.9 GB	Medium	C52
Q5_K_M	5	5.8 GB	High	C53
Q6_K	6	6.6 GB	High	C53
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Dolphin 2.9 8B on your machine.

Run

ollama run dolphin-llama3

Frequently asked questions

Can RTX 3080 12GB run Dolphin 2.9 8B?

Yes, RTX 3080 12GB can run Dolphin 2.9 8B with a B grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Dolphin 2.9 8B?

The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Dolphin 2.9 8B run at on RTX 3080 12GB?

On RTX 3080 12GB, Dolphin 2.9 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 3080 12GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on RTX 3080 12GB receives a B grade with 112.0 tok/s and 33K context.

What context window can Dolphin 2.9 8B use on RTX 3080 12GB?

On RTX 3080 12GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for RTX 3080 12GB See all hardware for Dolphin 2.9 8B

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<iframe src="https://willitrunai.com/embed/dolphin-2.9-8b-on-rtx-3080-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

112.0 tok/s

943 ms

33K

Coding

Runs well

112.0 tok/s

1729 ms

33K

Agentic Coding

Tight fit

112.0 tok/s

2514 ms

33K

Reasoning

Runs well

112.0 tok/s

2043 ms

33K

RAG

Tight fit

112.0 tok/s

3143 ms

33K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

C50

Q3_K_S

3.9 GB

Low

C51

NVFP4

4.5 GB

Medium

C52

Q4_K_M

4.9 GB

Medium

C52

Q5_K_M

5.8 GB

High

C53

Q6_K

6.6 GB

High

C53

Q8_0Best for your GPU

8.6 GB

Very High

C52

F16

16.4 GB

Maximum

Frequently asked questions

Can RTX 3080 12GB run Dolphin 2.9 8B?

Yes, RTX 3080 12GB can run Dolphin 2.9 8B with a B grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Dolphin 2.9 8B?

The recommended quantization for Dolphin 2.9 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Dolphin 2.9 8B run at on RTX 3080 12GB?

On RTX 3080 12GB, Dolphin 2.9 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 3080 12GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on RTX 3080 12GB receives a B grade with 112.0 tok/s and 33K context.

What context window can Dolphin 2.9 8B use on RTX 3080 12GB?

On RTX 3080 12GB, Dolphin 2.9 8B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.