Can OLMo 2 32B run on RTX A5500 24GB?

BARELY — Tight on Memory

A71Great

Estimated from fit model

OLMo 2 32B needs ~26.7 GB VRAM. RTX A5500 24GB has 24.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.7 GB, 19.8 tok/s, Very compromised (needs ~2 GB host RAM)

26.7 GB required24.0 GB available

111% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2 GB host RAM)

Decode

19.8 tok/s

TTFT

9768 ms

Safe context

Memory

26.7 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsOLMo 2 32B on RTX A5500 24GB
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: 19.8 tok/s decode · 9.8s TTFT (warm) · 50 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 10% 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.

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 2.0 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	A	Runs with offload (needs ~0.6 GB host RAM)	23.3 tok/s	4541 ms	4K
Coding	A	Very compromised (needs ~2 GB host RAM)	19.8 tok/s	9768 ms	4K
Agentic Coding	F	Too heavy	14.9 tok/s	18934 ms	4K
Reasoning	A	Very compromised (needs ~2 GB host RAM)	19.8 tok/s	11544 ms	4K
RAG	F	Too heavy	14.9 tok/s	23667 ms	4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A83
Q3_K_S	3	15.7 GB	Low	A82
NVFP4Best for your GPU	4	17.9 GB	Medium	A82
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

Your hardware

More models your RTX A5500 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	A	39 tok/s
Qwen 3.5 35B A3B	35B	A	52 tok/s

Frequently asked questions

Can RTX A5500 24GB run OLMo 2 32B?

Yes, RTX A5500 24GB can run OLMo 2 32B with a A grade (Very compromised (needs ~2 GB host RAM)). Expected decode speed: 19.8 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 26.7 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 RTX A5500 24GB?

On RTX A5500 24GB, OLMo 2 32B achieves approximately 19.8 tokens per second decode speed with a time-to-first-token of 9768ms using Q4_K_M quantization.

Can RTX A5500 24GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on RTX A5500 24GB receives a A grade with 19.8 tok/s and 4K context.

What context window can OLMo 2 32B use on RTX A5500 24GB?

On RTX A5500 24GB, 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.

What should I upgrade first if OLMo 2 32B feels slow on RTX A5500 24GB?

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 RTX A5500 24GB See all hardware for OLMo 2 32B

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