Mac Studio M3 Ultra 256GBOpção econômica
256 GB Unified (+76)
D
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.4.2 tok/s decodificação
~$6,999 MSRP
Can embeddinggemma 300M run on NVIDIA B200 180GB?
YES — Runs Great
D35Poor
Estimated from fit model
embeddinggemma 300M needs ~19.5 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q6_K quantization, expect ~4 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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.
Select quantization to explore
Q6_K (High quality) — 19.5 GB, 4.2 tok/s, Runs well
19.5 GB required180.0 GB available
11% VRAM used
Fit status
Runs well
Decode
4.2 tok/s
TTFT
46095 ms
Safe context
25.7M
Memory
19.5 GB / 180.0 GB
Memory breakdown
Weights0.2 GB
KV Cache0.1 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelsembeddinggemma 300M on NVIDIA B200 180GB
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
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.
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 | D | Runs well | 4.2 tok/s | 25143 ms | 12.8M |
| Coding | D | Runs well | 4.2 tok/s | 46095 ms | 25.7M |
| Agentic Coding | D | Runs well | 4.2 tok/s | 67048 ms | 51.4M |
| Reasoning | D | Runs well | 4.2 tok/s | 54476 ms | 25.7M |
| RAG | D | Runs well | 4.2 tok/s | 83810 ms | 51.4M |
Quantization options
How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | D37 |
Q3_K_S | 3 | 0.1 GB | Low | D37 |
NVFP4 | 4 | 0.2 GB | Medium | D37 |
Q4_K_M | 4 | 0.2 GB | Medium | D37 |
Q5_K_M | 5 | 0.2 GB | High | D37 |
Q6_K | 6 | 0.2 GB | High | D37 |
Q8_0 | 8 | 0.3 GB | Very High | D37 |
F16Best for your GPU | 16 | 0.6 GB | Maximum | D37 |
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 99Opções de upgrade
Hardware que roda bem embeddinggemma 300M
Frequently asked questions
Can NVIDIA B200 180GB run embeddinggemma 300M?
Yes, NVIDIA B200 180GB 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 19.5 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 NVIDIA B200 180GB?
On NVIDIA B200 180GB, embeddinggemma 300M achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46095ms using Q6_K quantization.
Can NVIDIA B200 180GB run embeddinggemma 300M for coding?
For coding workloads, embeddinggemma 300M on NVIDIA B200 180GB receives a D grade with 4.2 tok/s and 25.7M context.
What context window can embeddinggemma 300M use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, embeddinggemma 300M can safely use up to 25.7M 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 NVIDIA B200 180GB?
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.
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