Will It Run AI

Can embeddinggemma 300M run on MacBook Pro M3 Max 48GB?

YES — Runs Great

D37Poor
Estimated from fit model

embeddinggemma 300M needs ~6.4 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q6_K quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Memory bandwidth
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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

Q6_K (High quality) — 6.4 GB, 4.2 tok/s, Runs well
6.4 GB required34.6 GB available
18% VRAM used

Fit status

Runs well

Decode

4.2 tok/s

TTFT

46095 ms

Safe context

4.5M

Memory

6.4 GB / 34.6 GB

Memory breakdown

Weights0.2 GB
KV Cache0.1 GB
Runtime0.9 GB
Headroom5.2 GB

See how fast it feels

See how fast it feelsembeddinggemma 300M on MacBook Pro M3 Max 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: 4.2 tok/s decode · 46.1s TTFT (warm) · 11 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

WorkloadGradeFitDecodeTTFTContext
ChatDRuns well4.2 tok/s25143 ms2.3M
CodingDRuns well4.2 tok/s46095 ms4.5M
Agentic CodingDRuns well4.2 tok/s67048 ms9.0M
ReasoningDRuns well4.2 tok/s54476 ms4.5M
RAGDRuns well4.2 tok/s83810 ms9.0M

Quantization options

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowC42
Q3_K_S
3
0.1 GB
LowC42
NVFP4
4
0.2 GB
MediumC43
Q4_K_M
4
0.2 GB
MediumC43
Q5_K_M
5
0.2 GB
HighC43
Q6_K
6
0.2 GB
HighC43
Q8_0
8
0.3 GB
Very HighC43
F16Best for your GPU
16
0.6 GB
MaximumC43

Get started

Copy-paste commands to run embeddinggemma 300M on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "ggml-org/embeddinggemma-300M-GGUF" \ --hf-file "embeddinggemma-300M-GGUF-Q6_K.gguf" \ -c 4096 -ngl 99

Frequently asked questions

Can MacBook Pro M3 Max 48GB run embeddinggemma 300M?

Yes, MacBook Pro M3 Max 48GB can run embeddinggemma 300M with a D grade (Runs well). Expected decode speed: 4.2 tok/s.

How much VRAM does embeddinggemma 300M need?

embeddinggemma 300M (0.30000001192092896B parameters) requires approximately 6.4 GB of memory with Q6_K quantization.

What is the best quantization for embeddinggemma 300M?

The recommended quantization for embeddinggemma 300M is Q6_K, which balances quality and memory efficiency.

What speed will embeddinggemma 300M run at on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, embeddinggemma 300M achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46095ms using Q6_K quantization.

Can MacBook Pro M3 Max 48GB run embeddinggemma 300M for coding?

For coding workloads, embeddinggemma 300M on MacBook Pro M3 Max 48GB receives a D grade with 4.2 tok/s and 4.5M context.

What context window can embeddinggemma 300M use on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, embeddinggemma 300M can safely use up to 4.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if embeddinggemma 300M feels slow on MacBook Pro M3 Max 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.

Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for embeddinggemma 300M?

Not always. MacBook Pro M3 Max 48GB 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 M3 Max 48GBSee all hardware for embeddinggemma 300M
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