Can mxbai Embed Large run on MacBook Pro M4 Max 64GB?

YES — Runs Great

A72Great

Estimated from fit model

mxbai Embed Large needs ~10.3 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With F16 quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 10.3 GB, 4.7 tok/s, Runs well

10.3 GB required46.1 GB available

22% VRAM used

Fit status

Runs well

Decode

4.7 tok/s

TTFT

41279 ms

Safe context

512

Memory

10.3 GB / 46.1 GB

Memory breakdown

Weights0.7 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsmxbai Embed Large on MacBook Pro M4 Max 64GB
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

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

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	Fits
RTX 3090 24GB	24 GB	F16	4.7	Fits
RTX 4070 12GB	12 GB	F16	4.7	Fits
RTX 3060 12GB	12 GB	F16	4.7	Fits
RTX 4060 8GB	8 GB	F16	4.7	Fits
RX 7900 XTX 24GB	24 GB	F16	4.7	Fits
MacBook Pro M4 Max 128GB	128 GB	F16	4.7	Fits
Mac Studio M3 Ultra 256GB	256 GB	F16	4.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	F16	4.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	F16	4.7	Fits
MacBook Pro M4 Max 64GB	64 GB	F16	4.7	Fits
MacBook Pro M3 Max 64GB	64 GB	F16	4.7	Fits
MacBook Pro M1 Max 64GB	64 GB	F16	4.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	F16	4.7	Fits

Estimates for single-stream decoding at F16; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How mxbai Embed Large (0.33500000834465027B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 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	0.2 GB	Medium	A75
Q4_K_M	4	0.2 GB	Medium	A75
Q5_K_M	5	0.2 GB	High	A75
Q6_K	6	0.3 GB	High	A75
Q8_0	8	0.4 GB	Very High	A75
F16Best for your GPU	16	0.7 GB	Maximum	A75

Get started

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

Run

ollama run mxbai-embed-large

Your hardware

More models your MacBook Pro M4 Max 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	52 tok/s
Qwen 3.5 27B	27B	S	36.1 tok/s
Qwen 3.6 27B	27B	S	36.2 tok/s
Qwen 3.6 35B A3B	35B	S	43.7 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.8 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 64GB run mxbai Embed Large?

Yes, MacBook Pro M4 Max 64GB 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 10.3 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 MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, mxbai Embed Large achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41279ms using F16 quantization.

Can MacBook Pro M4 Max 64GB run mxbai Embed Large for coding?

For coding workloads, mxbai Embed Large on MacBook Pro M4 Max 64GB receives a A grade with 4.7 tok/s and 512 context.

What context window can mxbai Embed Large use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, 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 MacBook Pro M4 Max 64GB?

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.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for mxbai Embed Large?

Not always. MacBook Pro M4 Max 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M4 Max 64GB See all hardware for mxbai Embed Large

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