Can Aya Expanse 32B run on H100 NVL 188GB?

YES — Runs Great

C52Usable

Estimated from fit model

Aya Expanse 32B needs ~42.0 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~352 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 42.0 GB, 352.0 tok/s, Runs well

42.0 GB required188.0 GB available

22% VRAM used

Fit status

Runs well

Decode

352.0 tok/s

TTFT

550 ms

Safe context

Memory

42.0 GB / 188.0 GB

Memory breakdown

Weights19.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsAya Expanse 32B on H100 NVL 188GB
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: 352.0 tok/s decode · 550ms TTFT (warm) · 880 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	352.0 tok/s	350 ms	8K
Coding	C	Runs well	352.0 tok/s	550 ms	8K
Agentic Coding	C	Runs well	352.0 tok/s	800 ms	8K
Reasoning	C	Runs well	352.0 tok/s	650 ms	8K
RAG	C	Runs well	352.0 tok/s	1000 ms	8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C43
Q3_K_S	3	15.7 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run Aya Expanse 32B on your machine.

Run

ollama run aya-expanse:32b

Frequently asked questions

Can H100 NVL 188GB run Aya Expanse 32B?

Yes, H100 NVL 188GB can run Aya Expanse 32B with a C grade (Runs well). Expected decode speed: 352.0 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 42.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Aya Expanse 32B?

The recommended quantization for Aya Expanse 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Aya Expanse 32B run at on H100 NVL 188GB?

On H100 NVL 188GB, Aya Expanse 32B achieves approximately 352.0 tokens per second decode speed with a time-to-first-token of 550ms using Q4_K_M quantization.

Can H100 NVL 188GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on H100 NVL 188GB receives a C grade with 352.0 tok/s and 8K context.

What context window can Aya Expanse 32B use on H100 NVL 188GB?

On H100 NVL 188GB, Aya Expanse 32B 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 H100 NVL 188GB See all hardware for Aya Expanse 32B

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