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

YES — Runs Great

A83Great

Estimated from fit model

OLMo 2 32B needs ~32.6 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~156 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, 155.7 tok/s, Runs well

32.6 GB required80.0 GB available

41% VRAM used

Fit status

Runs well

Decode

155.7 tok/s

TTFT

1243 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 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: 155.7 tok/s decode · 1.2s TTFT (warm) · 389 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	155.7 tok/s	678 ms	4K
Coding	A	Runs well	155.7 tok/s	1243 ms	4K
Agentic Coding	A	Runs well	155.7 tok/s	1809 ms	4K
Reasoning	A	Runs well	155.7 tok/s	1470 ms	4K
RAG	A	Runs well	155.7 tok/s	2261 ms	4K

Inference speed

OLMo 2 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for OLMo 2 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~66 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	66.4	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	33.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	30.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	25.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.8	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	24.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	22.9	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	20.9	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	13.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	9.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.1	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on NVIDIA H100 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 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	28.9 tok/s
Qwen 3.5 122B A10B	122B	S	85.5 tok/s
Qwen 3.6 35B A3B	35B	S	357.6 tok/s
Qwen 3.5 35B A3B	35B	S	388.9 tok/s
Mistral Small 4 119B	119B	A	90.8 tok/s

Frequently asked questions

Can NVIDIA H100 80GB run OLMo 2 32B?

Yes, NVIDIA H100 80GB can run OLMo 2 32B with a A grade (Runs well). Expected decode speed: 155.7 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 80GB?

On NVIDIA H100 80GB, OLMo 2 32B achieves approximately 155.7 tokens per second decode speed with a time-to-first-token of 1243ms using Q4_K_M quantization.

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

For coding workloads, OLMo 2 32B on NVIDIA H100 80GB receives a A grade with 155.7 tok/s and 4K context.

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

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

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