Can OLMo 2 32B run on NVIDIA DGX Spark 128GB?

Q: Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for OLMo 2 32B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

YES — Runs Great

A74Great

Estimated from fit model

OLMo 2 32B needs ~37.7 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 37.7 GB, 9.1 tok/s, Runs well

37.7 GB required108.8 GB available

35% VRAM used

Fit status

Runs well

Decode

9.1 tok/s

TTFT

21362 ms

Safe context

Memory

37.7 GB / 108.8 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsOLMo 2 32B on NVIDIA DGX Spark 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: 9.1 tok/s decode · 21.4s TTFT (warm) · 23 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	9.1 tok/s	11652 ms	4K
Coding	A	Runs well	9.1 tok/s	21362 ms	4K
Agentic Coding	A	Runs well	9.1 tok/s	31072 ms	4K
Reasoning	A	Runs well	9.1 tok/s	25246 ms	4K
RAG	A	Runs well	9.1 tok/s	38841 ms	4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A72
Q3_K_S	3	15.7 GB	Low	A73
NVFP4	4

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 DGX Spark 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	2.4 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can NVIDIA DGX Spark 128GB run OLMo 2 32B?

Yes, NVIDIA DGX Spark 128GB can run OLMo 2 32B with a A grade (Runs well). Expected decode speed: 9.1 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 37.7 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 NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, OLMo 2 32B achieves approximately 9.1 tokens per second decode speed with a time-to-first-token of 21362ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on NVIDIA DGX Spark 128GB receives a A grade with 9.1 tok/s and 4K context.

What context window can OLMo 2 32B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, 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.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for OLMo 2 32B?

See all results for NVIDIA DGX Spark 128GB See all hardware for OLMo 2 32B

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