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URL: https://willitrunai.com/can-run/jina-embeddings-v3-on-a16-64gb

⇱ Jina Embeddings v3 on NVIDIA A16 64GB? YES

Can Jina Embeddings v3 run on NVIDIA A16 64GB?

YES — Runs Great

A75Great
○Estimated from fit model

Jina Embeddings v3 needs ~10.7 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With F16 quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:

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) — 10.7 GB, 8.0 tok/s, Runs well
10.7 GB required64.0 GB available
17% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24176 ms

Safe context

8K

Memory

10.7 GB / 64.0 GB

Memory breakdown

Weights1.2 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom6.4 GB

See how fast it feels

See how fast it feelsJina Embeddings v3 on NVIDIA A16 64GB
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.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns well8.0 tok/s13187 ms8K
CodingARuns well8.0 tok/s24176 ms8K
Agentic CodingARuns well8.0 tok/s35165 ms8K
ReasoningARuns well8.0 tok/s28571 ms8K
RAGARuns well8.0 tok/s43956 ms8K

Quantization options

How Jina Embeddings v3 (0.5720000267028809B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.2 GB
LowA77
Q3_K_S
3
0.3 GB
LowA77
NVFP4
4
0.3 GB
MediumA77
Q4_K_M
4
0.3 GB
MediumA77
Q5_K_M
5
0.4 GB
HighA77
Q6_K
6
0.5 GB
HighA77
Q8_0
8
0.6 GB
Very HighA77
F16Best for your GPU
16
1.2 GB
MaximumA77

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 NVIDIA A16 64GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct		30.5BS70.8 tok/s
👁 Alibaba
Qwen 3.5 27B		27BS30.7 tok/s
👁 Alibaba
Qwen 3.6 27B		27BS30.8 tok/s
👁 Alibaba
Qwen 3.6 35B A3B		35BS59.5 tok/s
👁 Alibaba
Qwen3-VL 30B A3B Instruct		30BS73.2 tok/s

Frequently asked questions

See all results for NVIDIA A16 64GBSee all hardware for Jina Embeddings v3