Can OLMo 2 32B run on NVIDIA T4 16GB?

YES — With Q2_K

B62Good

Estimated from fit model

OLMo 2 32B needs ~19.2 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q2_K quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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.

OLMo 2 32B at Q4_K_M needs 26.2 GB — too much for NVIDIA T4 16GB (16.0 GB). Runs at Q2_K (19.2 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.2 GB, exceeds 16.0 GB available

26.2 GB required16.0 GB available

164% VRAM needed

10.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.8 tok/s

TTFT

68269 ms

Safe context

Memory

26.2 GB / 16.0 GB

Offload

40%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsOLMo 2 32B on NVIDIA T4 16GB
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: 2.8 tok/s decode · 68.3s TTFT (warm) · 7 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.4 tok/s	31418 ms	4K
Coding	F	Too heavy	2.8 tok/s	68269 ms	4K
Agentic Coding	F	Too heavy	2.1 tok/s	134700 ms	4K
Reasoning	F	Too heavy	2.8 tok/s	80681 ms	4K
RAG	F	Too heavy	2.1 tok/s	168375 ms	4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	F0
Q3_K_S	3	15.7 GB	Low	F0
NVFP4	4	17.9 GB	Medium	F0
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

Get started

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

Run

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

Upgrade options

Hardware that runs OLMo 2 32B well

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+616)

Makes the model fit on the accelerator instead of staying completely out of reach.21.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 657%.

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.18.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 568%.

~$1,599 MSRP

RTX 5090 32GBBudget pick

32 GB VRAM (+16)1792 GB/s (+1472)

Makes the model fit on the accelerator instead of staying completely out of reach.66.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run OLMo 2 32B?

Yes, NVIDIA T4 16GB can run OLMo 2 32B at Q2_K quantization (Very compromised (needs ~2.1 GB host RAM)). The recommended Q4_K_M requires 26.2 GB which exceeds available memory, but at Q2_K it needs only 19.2 GB. Expected decode speed: 7.5 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 26.2 GB at Q4_K_M quantization. On NVIDIA T4 16GB, it fits at Q2_K using 19.2 GB.

What is the best quantization for OLMo 2 32B?

The recommended quantization is Q4_K_M, but on NVIDIA T4 16GB the best fitting quantization is Q2_K, which uses 19.2 GB.

What speed will OLMo 2 32B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, OLMo 2 32B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25849ms using Q2_K quantization.

Can NVIDIA T4 16GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on NVIDIA T4 16GB receives a F grade with 2.8 tok/s and 4K context.

What context window can OLMo 2 32B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, OLMo 2 32B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if OLMo 2 32B feels slow on NVIDIA T4 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for NVIDIA T4 16GB See all hardware for OLMo 2 32B

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

3.4 tok/s

31418 ms

Coding

Too heavy

2.8 tok/s

68269 ms

Agentic Coding

Too heavy

2.1 tok/s

134700 ms

Reasoning

Too heavy

2.8 tok/s

80681 ms

RAG

Too heavy

2.1 tok/s

168375 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

12.5 GB

Low

Q3_K_S

15.7 GB

Low

NVFP4

17.9 GB

Medium

Q4_K_M

19.5 GB

Medium

Q5_K_M

23.0 GB

High

Q6_K

26.2 GB

High

Q8_0

34.2 GB

Very High

F16

65.6 GB

Maximum

Frequently asked questions

Can NVIDIA T4 16GB run OLMo 2 32B?

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 26.2 GB at Q4_K_M quantization. On NVIDIA T4 16GB, it fits at Q2_K using 19.2 GB.

What is the best quantization for OLMo 2 32B?

The recommended quantization is Q4_K_M, but on NVIDIA T4 16GB the best fitting quantization is Q2_K, which uses 19.2 GB.

What speed will OLMo 2 32B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, OLMo 2 32B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25849ms using Q2_K quantization.

Can NVIDIA T4 16GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on NVIDIA T4 16GB receives a F grade with 2.8 tok/s and 4K context.

What context window can OLMo 2 32B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, OLMo 2 32B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 4K, but available memory constrains the safe maximum.

What should I upgrade first if OLMo 2 32B feels slow on NVIDIA T4 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.