Can Aya Expanse 8B run on RTX 3080 10GB?

YES — Tight Fit

B55Good

Estimated from fit model

Aya Expanse 8B needs ~8.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~96 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 8.7 GB, 96.0 tok/s, Tight fit

8.7 GB required10.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

96.0 tok/s

TTFT

2017 ms

Safe context

Memory

8.7 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsAya Expanse 8B on RTX 3080 10GB
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: 96.0 tok/s decode · 2.0s TTFT (warm) · 240 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	B	Runs well	96.0 tok/s	1100 ms	8K
Coding	B	Tight fit	96.0 tok/s	2017 ms	8K
Agentic Coding	C	Runs with offload (needs ~0.3 GB host RAM)	69.7 tok/s	4040 ms	8K
Reasoning	B	Tight fit	96.0 tok/s	2383 ms	8K
RAG	C	Runs with offload (needs ~0.3 GB host RAM)	69.7 tok/s	5050 ms

Quantization options

How Aya Expanse 8B (8B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C53
Q3_K_S	3	3.9 GB	Low	C54
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

RTX 3060 12GBBudget pick

12 GB VRAM (+2)

This setup is broadly balanced for this model.44 tok/s decode

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+2)

This setup is broadly balanced for this model.93.3 tok/s decode

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+2)

This setup is broadly balanced for this model.78.6 tok/s decode

~$599 MSRP

Frequently asked questions

Can RTX 3080 10GB run Aya Expanse 8B?

Yes, RTX 3080 10GB can run Aya Expanse 8B with a B grade (Tight fit). Expected decode speed: 96.0 tok/s.

How much VRAM does Aya Expanse 8B need?

Aya Expanse 8B (8B parameters) requires approximately 8.7 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 RTX 3080 10GB?

On RTX 3080 10GB, Aya Expanse 8B achieves approximately 96.0 tokens per second decode speed with a time-to-first-token of 2017ms using Q4_K_M quantization.

Can RTX 3080 10GB run Aya Expanse 8B for coding?

For coding workloads, Aya Expanse 8B on RTX 3080 10GB receives a B grade with 96.0 tok/s and 8K context.

What context window can Aya Expanse 8B use on RTX 3080 10GB?

On RTX 3080 10GB, 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 RTX 3080 10GB See all hardware for Aya Expanse 8B

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