Can Jina Embeddings v3 run on GTX 1660 Super 6GB?

YES — Tight Fit

A83Great

○Estimated from fit model

Jina Embeddings v3 needs ~4.9 GB VRAM. GTX 1660 Super 6GB has 6.0 GB. With F16 quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: 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

F16 (Maximum quality) — 4.9 GB, 8.0 tok/s, Tight fit

4.9 GB required6.0 GB available

82% VRAM used

Fit status

Tight fit

Decode

8.0 tok/s

TTFT

24176 ms

Safe context

Memory

4.9 GB / 6.0 GB

Memory breakdown

Weights1.2 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsJina Embeddings v3 on GTX 1660 Super 6GB
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: 8.0 tok/s decode · 24.2s TTFT (warm) · 20 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	A	Runs well	8.0 tok/s	13187 ms	8K
Coding	A	Tight fit	8.0 tok/s	24176 ms	8K
Agentic Coding	A	Very compromised (needs ~0.1 GB host RAM)	8.0 tok/s	35165 ms	8K
Reasoning	A	Tight fit	8.0 tok/s	28571 ms	8K
RAG	A	Very compromised (needs ~0.1 GB host RAM)	8.0 tok/s	43956 ms	8K

Quantization options

How Jina Embeddings v3 (0.5720000267028809B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.2 GB	Low	S87
Q3_K_S	3	0.3 GB	Low	S88
NVFP4	4	0.3 GB	Medium	S88
Q4_K_M	4	0.3 GB	Medium	S88
Q5_K_M	5	0.4 GB	High	S88
Q6_K	6	0.5 GB	High	S88
Q8_0	8	0.6 GB	Very High	S88
F16Best for your GPU	16	1.2 GB	Maximum	S90

Get started

Copy-paste commands to run Jina Embeddings v3 on your machine.

Run

ollama run jina/jina-embeddings-v3

Your hardware

More models your GTX 1660 Super 6GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 4B	4B	A	50.9 tok/s
👁 Microsoft Phi-4 Mini Reasoning 4B	3.8B	S	53.2 tok/s

Frequently asked questions

See all results for GTX 1660 Super 6GB See all hardware for Jina Embeddings v3

URL: https://willitrunai.com/can-run/jina-embeddings-v3-on-gtx-1660-super-6gb

⇱ Jina Embeddings v3 on GTX 1660 Super 6GB? TIGHT FIT