Will It Run AI

Can mxbai Embed Large run on NVIDIA GB200 192GB?

YES — Runs Great

B69Good
Estimated from fit model

mxbai Embed Large needs ~22.6 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Memory bandwidth
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) — 22.6 GB, 4.7 tok/s, Runs well
22.6 GB required192.0 GB available
12% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

22.6 GB / 192.0 GB

Memory breakdown

Weights0.7 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom19.2 GB

See how fast it feels

See how fast it feelsmxbai Embed Large 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: 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well4.7 tok/s22516 ms512
CodingBRuns well4.7 tok/s41279 ms512
Agentic CodingBRuns well4.7 tok/s60043 ms512
ReasoningBRuns well4.7 tok/s48785 ms512
RAGBRuns well4.7 tok/s75053 ms512

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowA71
Q3_K_S
3
0.2 GB
LowA71
NVFP4
4
0.2 GB
MediumA71
Q4_K_M
4
0.2 GB
MediumA71
Q5_K_M
5
0.2 GB
HighA71
Q6_K
6
0.3 GB
HighA71
Q8_0
8
0.4 GB
Very HighA71
F16Best for your GPU
16
0.7 GB
MaximumA71

Get started

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

Run

ollama run mxbai-embed-large

Frequently asked questions

Can NVIDIA GB200 192GB run mxbai Embed Large?

Yes, NVIDIA GB200 192GB can run mxbai Embed Large with a B grade (Runs well). Expected decode speed: 4.7 tok/s.

How much VRAM does mxbai Embed Large need?

mxbai Embed Large (0.33500000834465027B parameters) requires approximately 22.6 GB of memory with F16 quantization.

What is the best quantization for mxbai Embed Large?

The recommended quantization for mxbai Embed Large is F16, which balances quality and memory efficiency.

What speed will mxbai Embed Large run at on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, mxbai Embed Large achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.

Can NVIDIA GB200 192GB run mxbai Embed Large for coding?

For coding workloads, mxbai Embed Large on NVIDIA GB200 192GB receives a B grade with 4.7 tok/s and 512 context.

What context window can mxbai Embed Large use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, mxbai Embed Large can safely use up to 512 tokens of context. The model's official context limit is 512, but available memory constrains the safe maximum.

What should I upgrade first if mxbai Embed Large 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.

See all results for NVIDIA GB200 192GBSee all hardware for mxbai Embed Large
Embed this result▼

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

<iframe src="https://willitrunai.com/embed/mxbai-embed-large-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: