Can Nomic Embed Text v1.5 run on NVIDIA GB200 192GB?

Q: What should I upgrade first if Nomic Embed Text v1.5 feels slow on NVIDIA GB200 192GB?

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

B65Good

Estimated from fit model

Nomic Embed Text v1.5 needs ~21.2 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With F16 quantization, expect ~2 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 21.2 GB, 2.0 tok/s, Runs well

21.2 GB required192.0 GB available

11% VRAM used

Fit status

Runs well

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

21.2 GB / 192.0 GB

Memory breakdown

Weights0.3 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsNomic Embed Text v1.5 on NVIDIA GB200 192GB
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.

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

Quantization options

How Nomic Embed Text v1.5 (0.13699999451637268B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	B69
Q3_K_S	3	0.1 GB	Low	B69
NVFP4	4

Get started

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

Run

ollama run nomic-embed-text

Frequently asked questions

Can NVIDIA GB200 192GB run Nomic Embed Text v1.5?

Yes, NVIDIA GB200 192GB can run Nomic Embed Text v1.5 with a B 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 21.2 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 NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, 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 NVIDIA GB200 192GB run Nomic Embed Text v1.5 for coding?

For coding workloads, Nomic Embed Text v1.5 on NVIDIA GB200 192GB receives a B grade with 2.0 tok/s and 8K context.

What context window can Nomic Embed Text v1.5 use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, 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 NVIDIA GB200 192GB See all hardware for Nomic Embed Text v1.5

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/nomic-embed-text-v1.5-on-gb200-192gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: