Can Aya Expanse 8B run on RX 7600 XT 16GB?

YES — Runs Great

C53Usable

Estimated from fit model

Aya Expanse 8B needs ~9.3 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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

Q4_K_M (Medium quality) — 9.3 GB, 36.8 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

36.8 tok/s

TTFT

5261 ms

Safe context

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsAya Expanse 8B on RX 7600 XT 16GB
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: 36.8 tok/s decode · 5.3s TTFT (warm) · 92 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	36.8 tok/s	2870 ms	8K
Coding	C	Runs well	36.8 tok/s	5261 ms	8K
Agentic Coding	B	Runs well	36.8 tok/s	7653 ms	8K
Reasoning	C	Runs well	36.8 tok/s	6218 ms	8K
RAG	B	Runs well	36.8 tok/s	9566 ms	8K

Quantization options

How Aya Expanse 8B (8B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C49
NVFP4	4

Get started

Copy-paste commands to run Aya Expanse 8B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "CohereForAI/aya-expanse-8b" \
  --hf-file "aya-expanse-8b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs Aya Expanse 8B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 187%.105.7 tok/s decode

Raises estimated decode speed by about 187%.

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

~$899 MSRP

RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+352)

Raises estimated decode speed by about 199%.110 tok/s decode

Raises estimated decode speed by about 199%.

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

~$2,000 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run Aya Expanse 8B?

Yes, RX 7600 XT 16GB can run Aya Expanse 8B with a C grade (Runs well). Expected decode speed: 36.8 tok/s.

How much VRAM does Aya Expanse 8B need?

Aya Expanse 8B (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Aya Expanse 8B?

The recommended quantization for Aya Expanse 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Aya Expanse 8B run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Aya Expanse 8B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5261ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run Aya Expanse 8B for coding?

For coding workloads, Aya Expanse 8B on RX 7600 XT 16GB receives a C grade with 36.8 tok/s and 8K context.

What context window can Aya Expanse 8B use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Aya Expanse 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for Aya Expanse 8B

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