Can embeddinggemma 300M run on RX 5700 XT 8GB?

Q: What should I upgrade first if embeddinggemma 300M feels slow on RX 5700 XT 8GB?

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.

YES — Runs Great

D38Poor

Estimated from fit model

embeddinggemma 300M needs ~2.0 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q6_K quantization, expect ~4 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 2.0 GB, 4.2 tok/s, Runs well

2.0 GB required8.0 GB available

25% VRAM used

Fit status

Runs well

Decode

4.2 tok/s

TTFT

46095 ms

Safe context

969K

Memory

2.0 GB / 8.0 GB

Memory breakdown

Weights0.2 GB

KV Cache0.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsembeddinggemma 300M on RX 5700 XT 8GB
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	484K
Coding	D	Runs well	4.2 tok/s	46095 ms	969K
Agentic Coding	D	Runs well	4.2 tok/s	67048 ms	1.9M
Reasoning	D	Runs well	4.2 tok/s	54476 ms	969K
RAG	D	Runs well	4.2 tok/s	83810 ms	1.9M

Quantization options

How embeddinggemma 300M (0.30000001192092896B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.1 GB	Low	C49
Q3_K_S	3	0.1 GB	Low	C49
NVFP4	4

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

Upgrade options

Hardware that runs embeddinggemma 300M well

MacBook Pro M4 16GBBudget pick

16 GB Unified (+8)

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.

~$599 MSRP

MacBook Air M1 16GBBest value

16 GB Unified (+8)

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.

~$999 MSRP

Frequently asked questions

Can RX 5700 XT 8GB run embeddinggemma 300M?

Yes, RX 5700 XT 8GB 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 2.0 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 RX 5700 XT 8GB?

On RX 5700 XT 8GB, embeddinggemma 300M achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46095ms using Q6_K quantization.

Can RX 5700 XT 8GB run embeddinggemma 300M for coding?

For coding workloads, embeddinggemma 300M on RX 5700 XT 8GB receives a D grade with 4.2 tok/s and 969K context.

What context window can embeddinggemma 300M use on RX 5700 XT 8GB?

On RX 5700 XT 8GB, embeddinggemma 300M can safely use up to 969K 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 RX 5700 XT 8GB?

See all results for RX 5700 XT 8GB See all hardware for embeddinggemma 300M

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