Will It Run AI

Can embeddinggemma 300M run on MacBook Pro M3 Max 128GB?

YES — Runs Great

D36Poor
Estimated from fit model

embeddinggemma 300M needs ~15.1 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 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) — 15.1 GB, 4.2 tok/s, Runs well
15.1 GB required92.2 GB available
16% VRAM used

Fit status

Runs well

Decode

4.2 tok/s

TTFT

46095 ms

Safe context

12.4M

Memory

15.1 GB / 92.2 GB

Memory breakdown

Weights0.2 GB
KV Cache0.1 GB
Runtime0.9 GB
Headroom13.8 GB

See how fast it feels

See how fast it feelsembeddinggemma 300M on MacBook Pro M3 Max 128GB
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 ms6.2M
CodingDRuns well4.2 tok/s46095 ms12.4M
Agentic CodingDRuns well4.2 tok/s67048 ms24.7M
ReasoningDRuns well4.2 tok/s54476 ms12.4M
RAGDRuns well4.2 tok/s83810 ms24.7M

Quantization options

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.1 GB
LowD39
Q3_K_S
3
0.1 GB
LowD39
NVFP4
4
0.2 GB
MediumD39
Q4_K_M
4
0.2 GB
MediumD39
Q5_K_M
5
0.2 GB
HighD39
Q6_K
6
0.2 GB
HighD39
Q8_0
8
0.3 GB
Very HighD39
F16Best for your GPU
16
0.6 GB
MaximumD39

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 128GB run embeddinggemma 300M?

Yes, MacBook Pro M3 Max 128GB 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 15.1 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 128GB?

On MacBook Pro M3 Max 128GB, 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 128GB run embeddinggemma 300M for coding?

For coding workloads, embeddinggemma 300M on MacBook Pro M3 Max 128GB receives a D grade with 4.2 tok/s and 12.4M context.

What context window can embeddinggemma 300M use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, embeddinggemma 300M can safely use up to 12.4M 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 128GB?

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 128GB as fast as VRAM for embeddinggemma 300M?

Not always. MacBook Pro M3 Max 128GB 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 128GBSee all hardware for embeddinggemma 300M
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