Can embeddinggemma 300M run on NVIDIA GB200 192GB?

YES — Runs Great

D35Poor

Estimated from fit model

embeddinggemma 300M needs ~20.7 GB VRAM. NVIDIA GB200 192GB has 192.0 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.

Capabilities:

Select quantization to explore

Q6_K (High quality) — 20.7 GB, 4.2 tok/s, Runs well

20.7 GB required192.0 GB available

11% VRAM used

Fit status

Runs well

Decode

4.2 tok/s

TTFT

46095 ms

Safe context

27.4M

Memory

20.7 GB / 192.0 GB

Memory breakdown

Weights0.2 GB

KV Cache0.1 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsembeddinggemma 300M on NVIDIA GB200 192GB
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	13.7M
Coding	D	Runs well	4.2 tok/s	46095 ms	27.4M
Agentic Coding	D	Runs well	4.2 tok/s	67048 ms	54.8M
Reasoning	D	Runs well	4.2 tok/s	54476 ms	27.4M
RAG	D	Runs well	4.2 tok/s	83810 ms	54.8M

Quantization options

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 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 99

Frequently asked questions

Can NVIDIA GB200 192GB run embeddinggemma 300M?

Yes, NVIDIA GB200 192GB 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 20.7 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 GB200 192GB?

On NVIDIA GB200 192GB, 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 GB200 192GB run embeddinggemma 300M for coding?

For coding workloads, embeddinggemma 300M on NVIDIA GB200 192GB receives a D grade with 4.2 tok/s and 27.4M context.

What context window can embeddinggemma 300M use on NVIDIA GB200 192GB?

On NVIDIA GB200 192GB, embeddinggemma 300M can safely use up to 27.4M 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 NVIDIA GB200 192GB?

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.

See all results for NVIDIA GB200 192GB See all hardware for embeddinggemma 300M

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