Can Nomic Embed Text v1.5 run on RTX 2060 Super 8GB?

Q: What should I upgrade first if Nomic Embed Text v1.5 feels slow on RTX 2060 Super 8GB?

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.

YES — Runs Great

A70Great

Estimated from fit model

Nomic Embed Text v1.5 needs ~2.8 GB VRAM. RTX 2060 Super 8GB has 8.0 GB. With F16 quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 2.8 GB, 2.0 tok/s, Runs well

2.8 GB required8.0 GB available

35% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

2.8 GB / 8.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsNomic Embed Text v1.5 on RTX 2060 Super 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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 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 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

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	B	Runs well	2.0 tok/s	52800 ms	8K
Coding	A	Runs well	2.0 tok/s	96800 ms	8K
Agentic Coding	A	Runs well	2.0 tok/s	140800 ms	8K
Reasoning	A	Runs well	2.0 tok/s	114400 ms	8K
RAG	A	Runs well	2.0 tok/s	176000 ms	8K

Inference speed

Nomic Embed Text v1.5 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nomic Embed Text v1.5 at F16 across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~2 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	F16	2.0	Fits
RTX 4090 24GB	24 GB	F16	2.0	Fits
RTX 4080 Super 16GB	16 GB	F16	2.0

Quantization options

How Nomic Embed Text v1.5 (0.13699999451637268B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	A81
Q3_K_S	3	0.1 GB	Low	A81
NVFP4	4

Get started

Copy-paste commands to run Nomic Embed Text v1.5 on your machine.

Run

ollama run nomic-embed-text

Your hardware

More models your RTX 2060 Super 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 4B	4B	S	56 tok/s
Qwen 3 8B	8B	A	31.6 tok/s

Frequently asked questions

Can RTX 2060 Super 8GB run Nomic Embed Text v1.5?

Yes, RTX 2060 Super 8GB can run Nomic Embed Text v1.5 with a A grade (Runs well). Expected decode speed: 2.0 tok/s.

How much VRAM does Nomic Embed Text v1.5 need?

Nomic Embed Text v1.5 (0.13699999451637268B parameters) requires approximately 2.8 GB of memory with F16 quantization.

What is the best quantization for Nomic Embed Text v1.5?

The recommended quantization for Nomic Embed Text v1.5 is F16, which balances quality and memory efficiency.

What speed will Nomic Embed Text v1.5 run at on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Nomic Embed Text v1.5 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using F16 quantization.

Can RTX 2060 Super 8GB run Nomic Embed Text v1.5 for coding?

For coding workloads, Nomic Embed Text v1.5 on RTX 2060 Super 8GB receives a A grade with 2.0 tok/s and 8K context.

What context window can Nomic Embed Text v1.5 use on RTX 2060 Super 8GB?

On RTX 2060 Super 8GB, Nomic Embed Text v1.5 can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX 2060 Super 8GB See all hardware for Nomic Embed Text v1.5

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