Can mxbai Embed Large run on RTX 3080 10GB?

YES — Runs Great

A76Great

○Estimated from fit model

mxbai Embed Large needs ~4.4 GB VRAM. RTX 3080 10GB has 10.0 GB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Memory bandwidth

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

F16 (Maximum quality) — 4.4 GB, 4.7 tok/s, Runs well

4.4 GB required10.0 GB available

44% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

4.4 GB / 10.0 GB

Memory breakdown

Weights0.7 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsmxbai Embed Large on RTX 3080 10GB
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: 4.7 tok/s decode · 41.3s TTFT (warm) · 12 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 4.7 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	4.7 tok/s	22516 ms	512
Coding	A	Runs well	4.7 tok/s	41279 ms	512
Agentic Coding	A	Runs well	4.7 tok/s	60043 ms	512
Reasoning	A	Runs well	4.7 tok/s	48785 ms	512
RAG	A	Runs well	4.7 tok/s	75053 ms	512

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	A81
Q3_K_S	3	0.2 GB	Low	A81
NVFP4	4	0.2 GB	Medium	A81
Q4_K_M	4	0.2 GB	Medium	A81
Q5_K_M	5	0.2 GB	High	A81
Q6_K	6	0.3 GB	High	A82
Q8_0	8	0.4 GB	Very High	A82
F16Best for your GPU	16	0.7 GB	Maximum	A82

Get started

Copy-paste commands to run mxbai Embed Large on your machine.

Run

ollama run mxbai-embed-large

Your hardware

More models your RTX 3080 10GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	113.1 tok/s
👁 Alibaba Qwen 3.5 4B	4B	S	56 tok/s
👁 Alibaba Qwen 3 8B	8B	S	112 tok/s
👁 Microsoft Phi-4 Mini Reasoning 4B	3.8B	S	53.2 tok/s
👁 Jina AI Jina Embeddings v3	0.57B	A	8 tok/s

Frequently asked questions

See all results for RTX 3080 10GB See all hardware for mxbai Embed Large

URL: https://willitrunai.com/can-run/mxbai-embed-large-on-rtx-3080-10gb