Can Aya Expanse 8B run on NVIDIA A16 64GB?

YES — Runs Great

C48Usable

Estimated from fit model

Aya Expanse 8B needs ~14.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~103 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 14.1 GB, 103.1 tok/s, Runs well

14.1 GB required64.0 GB available

22% VRAM used

Fit status

Runs well

Decode

103.1 tok/s

TTFT

1878 ms

Safe context

Memory

14.1 GB / 64.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsAya Expanse 8B on NVIDIA A16 64GB
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: 103.1 tok/s decode · 1.9s TTFT (warm) · 258 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	103.1 tok/s	1024 ms	8K
Coding	C	Runs well	103.1 tok/s	1878 ms	8K
Agentic Coding	C	Runs well	103.1 tok/s	2731 ms	8K
Reasoning	C	Runs well	103.1 tok/s	2219 ms	8K
RAG	C	Runs well	103.1 tok/s	3414 ms	8K

Quantization options

How Aya Expanse 8B (8B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C42
Q3_K_S	3	3.9 GB	Low	C42
NVFP4	4	4.5 GB	Medium	C42
Q4_K_M	4	4.9 GB	Medium	C42
Q5_K_M	5	5.8 GB	High	C42
Q6_K	6	6.6 GB	High	C42
Q8_0	8	8.6 GB	Very High	C43
F16Best for your GPU	16	16.4 GB	Maximum	C44

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

Opciones de mejora

Hardware que ejecuta bien Aya Expanse 8B

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+32)

Este setup está bastante equilibrado para este modelo.82.6 tok/s decodificación

~$2,499 MSRP

Mac Studio M3 Ultra 96GBMejor relación calidad-precio

96 GB Unified (+32)819 GB/s (+219)

Este setup está bastante equilibrado para este modelo.112 tok/s decodificación

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Aya Expanse 8B?

Yes, NVIDIA A16 64GB can run Aya Expanse 8B with a C grade (Runs well). Expected decode speed: 103.1 tok/s.

How much VRAM does Aya Expanse 8B need?

Aya Expanse 8B (8B parameters) requires approximately 14.1 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Aya Expanse 8B achieves approximately 103.1 tokens per second decode speed with a time-to-first-token of 1878ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Aya Expanse 8B for coding?

For coding workloads, Aya Expanse 8B on NVIDIA A16 64GB receives a C grade with 103.1 tok/s and 8K context.

What context window can Aya Expanse 8B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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 NVIDIA A16 64GB See all hardware for Aya Expanse 8B

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