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URL: https://willitrunai.com/can-run/mxbai-embed-large-on-a5000-24gb

⇱ mxbai Embed Large on RTX A5000 24GB? YES

Can mxbai Embed Large run on RTX A5000 24GB?

YES — Runs Great

A72Great
○Estimated from fit model

mxbai Embed Large needs ~5.8 GB VRAM. RTX A5000 24GB has 24.0 GB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 5.8 GB, 4.7 tok/s, Runs well
5.8 GB required24.0 GB available
24% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

5.8 GB / 24.0 GB

Memory breakdown

Weights0.7 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsmxbai Embed Large on RTX A5000 24GB
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
ChatARuns well4.7 tok/s22516 ms512
CodingARuns well4.7 tok/s41279 ms512
Agentic CodingARuns well4.7 tok/s60043 ms512
ReasoningARuns well4.7 tok/s48785 ms512
RAGARuns well4.7 tok/s75053 ms512

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowA78
Q3_K_S
3
0.2 GB
LowA78
NVFP4
4
0.2 GB
MediumA78
Q4_K_M
4
0.2 GB
MediumA78
Q5_K_M
5
0.2 GB
HighA78
Q6_K
6
0.3 GB
HighA78
Q8_0
8
0.4 GB
Very HighA78
F16Best for your GPU
16
0.7 GB
MaximumA78

Get started

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

Run

ollama run mxbai-embed-large

Your hardware

More models your RTX A5000 24GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct		30.5BS81.3 tok/s
👁 Alibaba
Qwen 3.5 27B		27BS35.3 tok/s
👁 Alibaba
Qwen 3.6 27B		27BS35.4 tok/s
👁 Alibaba
Qwen3-VL 30B A3B Instruct		30BS84.1 tok/s
👁 Alibaba
Qwen 3.5 9B		9BS105.3 tok/s

Frequently asked questions

See all results for RTX A5000 24GBSee all hardware for mxbai Embed Large