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URL: https://willitrunai.com/can-run/olmo-2-32b-on-instinct-mi250-128gb

⇱ OLMo 2 32B on AMD Instinct MI250 128GB? YES

Can OLMo 2 32B run on AMD Instinct MI250 128GB?

YES — Runs Great

A80Great
○Estimated from fit model

OLMo 2 32B needs ~37.1 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~120 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 37.1 GB, 120.4 tok/s, Runs well
37.1 GB required128.0 GB available
29% VRAM used

Fit status

Runs well

Decode

120.4 tok/s

TTFT

1608 ms

Safe context

4K

Memory

37.1 GB / 128.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.9 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsOLMo 2 32B on AMD Instinct MI250 128GB
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: 120.4 tok/s decode · 1.6s TTFT (warm) · 301 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well120.4 tok/s877 ms4K
CodingARuns well120.4 tok/s1608 ms4K
Agentic CodingARuns well120.4 tok/s2339 ms4K
ReasoningARuns well120.4 tok/s1900 ms4K
RAGARuns well120.4 tok/s2924 ms4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowA71
Q3_K_S
3
15.7 GB
LowA71
NVFP4
4
17.9 GB
MediumA71
Q4_K_M
4
19.5 GB
MediumA71
Q5_K_M
5
23.0 GB
HighA72
Q6_K
6
26.2 GB
HighA72
Q8_0
8
34.2 GB
Very HighA74
F16Best for your GPU
16
65.6 GB
MaximumA79

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 AMD Instinct MI250 128GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct		123BS31.5 tok/s
👁 Alibaba
Qwen 3.5 122B A10B		122BS87.5 tok/s
👁 Alibaba
Qwen 3.6 35B A3B		35BS276.5 tok/s
👁 Alibaba
Qwen 3.5 35B A3B		35BS300.7 tok/s
👁 Mistral
Mistral Small 4 119B		119BS94.8 tok/s

Frequently asked questions

See all results for AMD Instinct MI250 128GBSee all hardware for OLMo 2 32B