Can OLMo 2 32B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

A83Great

Estimated from fit model

OLMo 2 32B needs ~32.6 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~93 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) — 32.6 GB, 93.0 tok/s, Runs well

32.6 GB required80.0 GB available

41% VRAM used

Fit status

Runs well

Decode

93.0 tok/s

TTFT

2083 ms

Safe context

Memory

32.6 GB / 80.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsOLMo 2 32B on NVIDIA H100 PCIe 80GB
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: 93.0 tok/s decode · 2.1s TTFT (warm) · 232 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	A	Runs well	93.0 tok/s	1136 ms	4K
Coding	A	Runs well	93.0 tok/s	2083 ms	4K
Agentic Coding	A	Runs well	93.0 tok/s	3030 ms	4K
Reasoning	A	Runs well	93.0 tok/s	2462 ms	4K
RAG	A	Runs well	93.0 tok/s	3787 ms	4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A73
Q3_K_S	3	15.7 GB	Low	A73
NVFP4	4	17.9 GB	Medium	A74
Q4_K_M	4	19.5 GB	Medium	A74
Q5_K_M	5	23.0 GB	High	A75
Q6_K	6	26.2 GB	High	A75
Q8_0	8	34.2 GB	Very High	A77
F16Best for your GPU	16	65.6 GB	Maximum	A80

Get started

Copy-paste commands to run OLMo 2 32B on your machine.

Run

lms load OLMo-2-0325-32B-Instruct && lms server start

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen 3.5 122B A10B	122B	A	44.5 tok/s
Qwen 3.6 35B A3B	35B	S	213.5 tok/s
Qwen 3.5 35B A3B	35B	S	232.2 tok/s
Mistral Small 4 119B	119B	A	47 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run OLMo 2 32B?

Yes, NVIDIA H100 PCIe 80GB can run OLMo 2 32B with a A grade (Runs well). Expected decode speed: 93.0 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 32.6 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 32B?

The recommended quantization for OLMo 2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 32B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, OLMo 2 32B achieves approximately 93.0 tokens per second decode speed with a time-to-first-token of 2083ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on NVIDIA H100 PCIe 80GB receives a A grade with 93.0 tok/s and 4K context.

What context window can OLMo 2 32B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, OLMo 2 32B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for OLMo 2 32B

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