Can aya expanse 8b run on NVIDIA L4 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

aya expanse 8b needs ~9.4 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.4 GB, 40.0 tok/s, Runs well

9.4 GB required24.0 GB available

39% VRAM used

Fit status

Runs well

Decode

40.0 tok/s

TTFT

4845 ms

Safe context

265K

Memory

9.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsaya expanse 8b on NVIDIA L4 24GB
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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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	40.0 tok/s	2643 ms	265K
Coding	C	Runs well	40.0 tok/s	4845 ms	265K
Agentic Coding	C	Runs well	40.0 tok/s	7047 ms	265K
Reasoning	C	Runs well	40.0 tok/s	5726 ms	265K
RAG	C	Runs well	40.0 tok/s	8809 ms	265K

Quantization options

How aya expanse 8b (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C44
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C45
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C46
Q8_0	8	8.6 GB	Very High	C47
F16Best for your GPU	16	16.4 GB	Maximum	C50

Get started

Copy-paste commands to run aya expanse 8b on your machine.

Run

lms load hf-bartowski--aya-expanse-8b-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien aya expanse 8b

RTX 5090 32GBOpción económica

32 GB VRAM (+8)1792 GB/s (+1492)

Sube la velocidad estimada de decodificación alrededor de un 180%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 180%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMejor relación calidad-precio

32 GB VRAM (+8)896 GB/s (+596)

Sube la velocidad estimada de decodificación alrededor de un 180%.112 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 180%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

RTX 5000 Ada 32GBMejora NVIDIA

32 GB VRAM (+8)576 GB/s (+276)

Sube la velocidad estimada de decodificación alrededor de un 136%.94.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 136%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run aya expanse 8b?

Yes, NVIDIA L4 24GB can run aya expanse 8b with a C grade (Runs well). Expected decode speed: 40.0 tok/s.

How much VRAM does aya expanse 8b need?

aya expanse 8b (8B parameters) requires approximately 9.4 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 L4 24GB?

On NVIDIA L4 24GB, aya expanse 8b achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4845ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run aya expanse 8b for coding?

For coding workloads, aya expanse 8b on NVIDIA L4 24GB receives a C grade with 40.0 tok/s and 265K context.

What context window can aya expanse 8b use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, aya expanse 8b can safely use up to 265K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for aya expanse 8b

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--aya-expanse-8b-gguf-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: