Can Aya Expanse 8B run on RX 7900 XT 20GB?

YES — Runs Great

C54Usable

Estimated from fit model

Aya Expanse 8B needs ~9.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~106 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 9.7 GB, 105.7 tok/s, Runs well

9.7 GB required20.0 GB available

49% VRAM used

Fit status

Runs well

Decode

105.7 tok/s

TTFT

1831 ms

Safe context

Memory

9.7 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsAya Expanse 8B on RX 7900 XT 20GB
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: 105.7 tok/s decode · 1.8s TTFT (warm) · 264 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	105.7 tok/s	999 ms	8K
Coding	C	Runs well	105.7 tok/s	1831 ms	8K
Agentic Coding	B	Runs well	105.7 tok/s	2663 ms	8K
Reasoning	C	Runs well	105.7 tok/s	2164 ms	8K
RAG	B	Runs well	105.7 tok/s	3329 ms	8K

Inference speed

Aya Expanse 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Aya Expanse 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	152.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	128.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	107.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	102.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	96.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	96.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	82.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	82.6	Fits
RTX 4070 12GB	12 GB	Q4_K_M	76.6	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	52.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	48.5	Fits
RTX 3060 12GB	12 GB	Q4_K_M	44.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	42.6	Fits
RTX 4060 8GB	8 GB	Q4_K_M	26.3	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Aya Expanse 8B (8B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C48
Q5_K_M	5	5.8 GB	High	C49
Q6_K	6	6.6 GB	High	C50
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0

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

Frequently asked questions

Can RX 7900 XT 20GB run Aya Expanse 8B?

Yes, RX 7900 XT 20GB can run Aya Expanse 8B with a C grade (Runs well). Expected decode speed: 105.7 tok/s.

How much VRAM does Aya Expanse 8B need?

Aya Expanse 8B (8B parameters) requires approximately 9.7 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 7900 XT 20GB?

On RX 7900 XT 20GB, Aya Expanse 8B achieves approximately 105.7 tokens per second decode speed with a time-to-first-token of 1831ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Aya Expanse 8B for coding?

For coding workloads, Aya Expanse 8B on RX 7900 XT 20GB receives a C grade with 105.7 tok/s and 8K context.

What context window can Aya Expanse 8B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, 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 7900 XT 20GB See all hardware for Aya Expanse 8B

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