Can embeddinggemma 300M run on RTX 6000 Ada 48GB?

YES — Runs Great

D35Poor

Estimated from fit model

embeddinggemma 300M needs ~6.3 GB VRAM. RTX 6000 Ada 48GB has 48.0 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) — 6.3 GB, 4.2 tok/s, Runs well

6.3 GB required48.0 GB available

13% VRAM used

Fit status

Runs well

Decode

4.2 tok/s

TTFT

46095 ms

Safe context

6.7M

Memory

6.3 GB / 48.0 GB

Memory breakdown

Weights0.2 GB

KV Cache0.1 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsembeddinggemma 300M on RTX 6000 Ada 48GB
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	3.3M
Coding	D	Runs well	4.2 tok/s	46095 ms	6.7M
Agentic Coding	D	Runs well	4.2 tok/s	67048 ms	13.4M
Reasoning	D	Runs well	4.2 tok/s	54476 ms	6.7M
RAG	D	Runs well	4.2 tok/s	83810 ms	13.4M

Quantization options

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	C41
Q3_K_S	3	0.1 GB	Low	C41
NVFP4	4	0.2 GB	Medium	C41
Q4_K_M	4	0.2 GB	Medium	C41
Q5_K_M	5	0.2 GB	High	C41
Q6_K	6	0.2 GB	High	C41
Q8_0	8	0.3 GB	Very High	C41
F16Best for your GPU	16	0.6 GB	Maximum	C41

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

Opções de upgrade

Hardware que roda bem embeddinggemma 300M

MacBook Pro M4 Max 96GBOpção econômica

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.4.2 tok/s decodificação

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

MacBook Pro M3 Max 128GBMelhor custo-benefício

128 GB Unified (+80)

Adds memory headroom for longer context windows and future model growth.4.2 tok/s decodificação

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

NVIDIA DGX Spark 128GBUpgrade NVIDIA

128 GB Unified (+80)

Adds memory headroom for longer context windows and future model growth.4.2 tok/s decodificação

Adds memory headroom for longer context windows and future model growth.

Frequently asked questions

Can RTX 6000 Ada 48GB run embeddinggemma 300M?

Yes, RTX 6000 Ada 48GB 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 6.3 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 RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, embeddinggemma 300M achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46095ms using Q6_K quantization.

Can RTX 6000 Ada 48GB run embeddinggemma 300M for coding?

For coding workloads, embeddinggemma 300M on RTX 6000 Ada 48GB receives a D grade with 4.2 tok/s and 6.7M context.

What context window can embeddinggemma 300M use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, embeddinggemma 300M can safely use up to 6.7M 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 RTX 6000 Ada 48GB?

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 RTX 6000 Ada 48GB See all hardware for embeddinggemma 300M

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