Mac mini M4 64GBBudget pick
64 GB Unified (+40)
D
Adds memory headroom for longer context windows and future model growth.4.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
embeddinggemma 300M needs ~3.9 GB VRAM. RTX 5090 Laptop 24GB has 24.0 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
3.2M
Memory
3.9 GB / 24.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 4.2 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
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 | 1.6M |
| Coding | D | Runs well | 4.2 tok/s | 46095 ms | 3.2M |
| Agentic Coding | D | Runs well | 4.2 tok/s | 67048 ms | 6.4M |
| Reasoning | D | Runs well | 4.2 tok/s | 54476 ms | 3.2M |
| RAG | D | Runs well | 4.2 tok/s | 83810 ms | 6.4M |
How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | C44 |
Q3_K_S | 3 | 0.1 GB | Low | C44 |
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 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, RTX 5090 Laptop 24GB can run embeddinggemma 300M with a D grade (Runs well). Expected decode speed: 4.2 tok/s.
embeddinggemma 300M (0.30000001192092896B parameters) requires approximately 3.9 GB of memory with Q6_K quantization.
The recommended quantization for embeddinggemma 300M is Q6_K, which balances quality and memory efficiency.
On RTX 5090 Laptop 24GB, 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 RTX 5090 Laptop 24GB receives a D grade with 4.2 tok/s and 3.2M context.
On RTX 5090 Laptop 24GB, embeddinggemma 300M can safely use up to 3.2M 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-rtx-5090-laptop-24gb" 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 |
| C44 |
Q4_K_M | 4 | 0.2 GB | Medium | C44 |
Q5_K_M | 5 | 0.2 GB | High | C44 |
Q6_K | 6 | 0.2 GB | High | C44 |
Q8_0 | 8 | 0.3 GB | Very High | C44 |
F16Best for your GPU | 16 | 0.6 GB | Maximum | C44 |
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.