Can Aya Expanse 32B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

C53Usable

Estimated from fit model

Aya Expanse 32B needs ~37.3 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~225 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) — 37.3 GB, 224.6 tok/s, Runs well

37.3 GB required141.0 GB available

26% VRAM used

Fit status

Runs well

Decode

224.6 tok/s

TTFT

862 ms

Safe context

Memory

37.3 GB / 141.0 GB

Memory breakdown

Weights19.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsAya Expanse 32B on NVIDIA H200 PCIe 141GB
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: 224.6 tok/s decode · 862ms TTFT (warm) · 562 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	224.6 tok/s	470 ms	8K
Coding	C	Runs well	224.6 tok/s	862 ms	8K
Agentic Coding	C	Runs well	224.6 tok/s	1254 ms	8K
Reasoning	C	Runs well	224.6 tok/s	1019 ms	8K
RAG	C	Runs well	224.6 tok/s	1567 ms	8K

Quantization options

How Aya Expanse 32B (32B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C44
Q3_K_S	3	15.7 GB	Low	C44
NVFP4	4	17.9 GB	Medium	C44
Q4_K_M	4	19.5 GB	Medium	C44
Q5_K_M	5	23.0 GB	High	C44
Q6_K	6	26.2 GB	High	C45
Q8_0	8	34.2 GB	Very High	C46
F16Best for your GPU	16	65.6 GB	Maximum	C51

Get started

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

Run

ollama run aya-expanse:32b

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Aya Expanse 32B?

Yes, NVIDIA H200 PCIe 141GB can run Aya Expanse 32B with a C grade (Runs well). Expected decode speed: 224.6 tok/s.

How much VRAM does Aya Expanse 32B need?

Aya Expanse 32B (32B parameters) requires approximately 37.3 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 NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Aya Expanse 32B achieves approximately 224.6 tokens per second decode speed with a time-to-first-token of 862ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Aya Expanse 32B for coding?

For coding workloads, Aya Expanse 32B on NVIDIA H200 PCIe 141GB receives a C grade with 224.6 tok/s and 8K context.

What context window can Aya Expanse 32B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, 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 NVIDIA H200 PCIe 141GB See all hardware for Aya Expanse 32B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/aya-expanse-32b-on-h200-pcie-141gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: