Can mxbai Embed Large run on NVIDIA L40 48GB?

Q: What should I upgrade first if mxbai Embed Large feels slow on NVIDIA L40 48GB?

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

mxbai Embed Large needs ~8.2 GB VRAM. NVIDIA L40 48GB has 48.0 GB. With F16 quantization, expect ~5 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) — 8.2 GB, 4.7 tok/s, Runs well

8.2 GB required48.0 GB available

17% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

8.2 GB / 48.0 GB

Memory breakdown

Weights0.7 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsmxbai Embed Large on NVIDIA L40 48GB
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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	4.7 tok/s	22516 ms	512
Coding	A	Runs well	4.7 tok/s	41279 ms	512
Agentic Coding	A	Runs well	4.7 tok/s	60043 ms	512
Reasoning	A	Runs well	4.7 tok/s	48785 ms	512
RAG	A	Runs well	4.7 tok/s	75053 ms	512

Inference speed

mxbai Embed Large inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for mxbai Embed Large at F16 across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~5 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	4.7	Fits
RTX 4090 24GB	24 GB	F16	4.7	Fits
RTX 4080 Super 16GB	16 GB	F16	4.7

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on NVIDIA L40 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	A75
Q3_K_S	3	0.2 GB	Low	A75
NVFP4	4

Get started

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

Run

ollama run mxbai-embed-large

Your hardware

More models your NVIDIA L40 48GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	101.9 tok/s
Qwen 3.5 27B	27B	S	44.2 tok/s

Frequently asked questions

Can NVIDIA L40 48GB run mxbai Embed Large?

Yes, NVIDIA L40 48GB can run mxbai Embed Large with a A 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 8.2 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 L40 48GB?

On NVIDIA L40 48GB, 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 L40 48GB run mxbai Embed Large for coding?

For coding workloads, mxbai Embed Large on NVIDIA L40 48GB receives a A grade with 4.7 tok/s and 512 context.

What context window can mxbai Embed Large use on NVIDIA L40 48GB?

On NVIDIA L40 48GB, 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 L40 48GB?

See all results for NVIDIA L40 48GB See all hardware for mxbai Embed Large

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