Will It Run AI

Can Dolphin 2.9 8B run on RTX 3050 8GB?

BARELY — Tight on Memory

D39Poor
Estimated from fit model

Dolphin 2.9 8B needs ~8.8 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Host offload
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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) — 8.8 GB, 19.8 tok/s, Very compromised (needs ~0.5 GB host RAM)
8.8 GB required8.0 GB available
110% VRAM needed

0.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.5 GB host RAM)

Decode

19.8 tok/s

TTFT

9764 ms

Safe context

9K

Memory

8.8 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsDolphin 2.9 8B on RTX 3050 8GB
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: 19.8 tok/s decode · 9.8s TTFT (warm) · 50 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 10% 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload30.3 tok/s3486 ms9K
CodingDVery compromised18.4 tok/s10496 ms9K
Agentic CodingFToo heavy12.1 tok/s23248 ms9K
ReasoningDVery compromised18.4 tok/s12405 ms9K
RAGFToo heavy12.1 tok/s29060 ms9K

Quantization options

How Dolphin 2.9 8B (8B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC54
Q3_K_S
3
3.9 GB
LowC54
NVFP4
4
4.5 GB
MediumC54
Q4_K_MBest for your GPU
4
4.9 GB
MediumC54
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

Get started

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

Run

ollama run dolphin-llama3

Opciones de mejora

Hardware que ejecuta bien Dolphin 2.9 8B

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+4)360 GB/s (+136)
B
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.52.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 164%.

~$329 MSRP

NVIDIARTX 5060 Ti 16GBMejor relación calidad-precio
16 GB VRAM (+8)448 GB/s (+224)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.61.2 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 209%.

~$449 MSRP

NVIDIARTX 4060 Ti 16GBMejora NVIDIA
16 GB VRAM (+8)288 GB/s (+64)
C
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.46.3 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 134%.

~$499 MSRP

Frequently asked questions

Can RTX 3050 8GB run Dolphin 2.9 8B?

Yes, RTX 3050 8GB can run Dolphin 2.9 8B with a D grade (Very compromised). Expected decode speed: 18.4 tok/s.

How much VRAM does Dolphin 2.9 8B need?

Dolphin 2.9 8B (8B parameters) requires approximately 8.8 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 3050 8GB?

On RTX 3050 8GB, Dolphin 2.9 8B achieves approximately 18.4 tokens per second decode speed with a time-to-first-token of 10496ms using Q4_K_M quantization.

Can RTX 3050 8GB run Dolphin 2.9 8B for coding?

For coding workloads, Dolphin 2.9 8B on RTX 3050 8GB receives a D grade with 18.4 tok/s and 9K context.

What context window can Dolphin 2.9 8B use on RTX 3050 8GB?

On RTX 3050 8GB, Dolphin 2.9 8B can safely use up to 9K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Dolphin 2.9 8B feels slow on RTX 3050 8GB?

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.

See all results for RTX 3050 8GBSee all hardware for Dolphin 2.9 8B
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