Can OLMo 2 7B run on NVIDIA H800 80GB?

YES — Runs Great

B67Good

Estimated from fit model

OLMo 2 7B needs ~15.4 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~98 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) — 15.4 GB, 98.0 tok/s, Runs well

15.4 GB required80.0 GB available

19% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

Memory

15.4 GB / 80.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsOLMo 2 7B on NVIDIA H800 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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	B	Runs well	98.0 tok/s	1078 ms	4K
Coding	B	Runs well	98.0 tok/s	1976 ms	4K
Agentic Coding	B	Runs well	98.0 tok/s	2873 ms	4K
Reasoning	B	Runs well	98.0 tok/s	2335 ms	4K
RAG	B	Runs well	98.0 tok/s	3592 ms	4K

Quantization options

How OLMo 2 7B (7B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B60
Q3_K_S	3	3.4 GB	Low	B60
NVFP4	4	3.9 GB	Medium	B60
Q4_K_M	4	4.3 GB	Medium	B60
Q5_K_M	5	5.0 GB	High	B60
Q6_K	6	5.7 GB	High	B60
Q8_0	8	7.5 GB	Very High	B61
F16Best for your GPU	16	14.3 GB	Maximum	B61

Get started

Copy-paste commands to run OLMo 2 7B on your machine.

Run

ollama run olmo2:7b

Upgrade options

Hardware that runs OLMo 2 7B well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.98 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA H800 80GB run OLMo 2 7B?

Yes, NVIDIA H800 80GB can run OLMo 2 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does OLMo 2 7B need?

OLMo 2 7B (7B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 7B?

The recommended quantization for OLMo 2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 7B run at on NVIDIA H800 80GB?

On NVIDIA H800 80GB, OLMo 2 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA H800 80GB run OLMo 2 7B for coding?

For coding workloads, OLMo 2 7B on NVIDIA H800 80GB receives a B grade with 98.0 tok/s and 4K context.

What context window can OLMo 2 7B use on NVIDIA H800 80GB?

On NVIDIA H800 80GB, OLMo 2 7B 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 H800 80GB See all hardware for OLMo 2 7B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

98.0 tok/s

1078 ms

Coding

Runs well

98.0 tok/s

1976 ms

Agentic Coding

Runs well

98.0 tok/s

2873 ms

Reasoning

Runs well

98.0 tok/s

2335 ms

RAG

Runs well

98.0 tok/s

3592 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

B60

Q3_K_S

3.4 GB

Low

B60

NVFP4

3.9 GB

Medium

B60

Q4_K_M

4.3 GB

Medium

B60

Q5_K_M

5.0 GB

High

B60

Q6_K

5.7 GB

High

B60

Q8_0

7.5 GB

Very High

B61

F16Best for your GPU

14.3 GB

Maximum

B61

Frequently asked questions

Can NVIDIA H800 80GB run OLMo 2 7B?

Yes, NVIDIA H800 80GB can run OLMo 2 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does OLMo 2 7B need?

OLMo 2 7B (7B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 7B?

The recommended quantization for OLMo 2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 7B run at on NVIDIA H800 80GB?

On NVIDIA H800 80GB, OLMo 2 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can NVIDIA H800 80GB run OLMo 2 7B for coding?

For coding workloads, OLMo 2 7B on NVIDIA H800 80GB receives a B grade with 98.0 tok/s and 4K context.

What context window can OLMo 2 7B use on NVIDIA H800 80GB?

On NVIDIA H800 80GB, OLMo 2 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.