embeddinggemma 300M needs ~3.0 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q6_K quantization, expect ~4 tok/s.
Operating mode
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.
Select quantization to explore
Fit status
Runs well
Decode
4.2 tok/s
TTFT
46095 ms
Safe context
1.4M
Memory
3.0 GB / 11.5 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs well | 4.2 tok/s | 25143 ms | 692K |
| Coding | D | Runs well | 4.2 tok/s | 46095 ms | 1.4M |
| Agentic Coding | D | Runs well | 4.2 tok/s | 67048 ms | 2.8M |
| Reasoning | D | Runs well | 4.2 tok/s | 54476 ms | 1.4M |
| RAG | D | Runs well | 4.2 tok/s | 83810 ms | 2.8M |
How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | C47 |
Q3_K_S | 3 | 0.1 GB | Low | C47 |
NVFP4 | 4 |
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 99Yes, MacBook Pro M1 Pro 16GB can run embeddinggemma 300M with a D grade (Runs well). Expected decode speed: 4.2 tok/s.
embeddinggemma 300M (0.30000001192092896B parameters) requires approximately 3.0 GB of memory with Q6_K quantization.
The recommended quantization for embeddinggemma 300M is Q6_K, which balances quality and memory efficiency.
On MacBook Pro M1 Pro 16GB, embeddinggemma 300M achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46095ms using Q6_K quantization.
For coding workloads, embeddinggemma 300M on MacBook Pro M1 Pro 16GB receives a D grade with 4.2 tok/s and 1.4M context.
On MacBook Pro M1 Pro 16GB, embeddinggemma 300M can safely use up to 1.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-ggml-org--embeddinggemma-300m-gguf-on-m1-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.2 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 0.2 GB | Medium | C47 |
Q5_K_M | 5 | 0.2 GB | High | C47 |
Q6_K | 6 | 0.2 GB | High | C47 |
Q8_0 | 8 | 0.3 GB | Very High | C47 |
F16Best for your GPU | 16 | 0.6 GB | Maximum | C47 |
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.
Not always. MacBook Pro M1 Pro 16GB 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.