OLMo 2 32B needs ~19.2 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q2_K quantization, expect ~24 tok/s.
Operating mode
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.
Select quantization to explore
10.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.5 tok/s
TTFT
20466 ms
Safe context
4K
Memory
26.2 GB / 16.0 GB
Offload
40%
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.
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 | F | Too heavy | 11.1 tok/s | 9485 ms | 4K |
| Coding | F | Too heavy | 9.5 tok/s | 20466 ms | 4K |
| Agentic Coding | F | Too heavy | 7.1 tok/s | 39873 ms | 4K |
| Reasoning | F | Too heavy | 9.5 tok/s | 24187 ms | 4K |
| RAG | F | Too heavy | 7.1 tok/s | 49842 ms | 4K |
How OLMo 2 32B (32B params) fits at each quantization level on RTX 5080 Laptop 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 |
Copy-paste commands to run OLMo 2 32B on your machine.
Run
lms load OLMo-2-0325-32B-Instruct && lms server startUpgrade options
RTX 3090 24GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 123%.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 161%.
~$1,599 MSRP
RTX 5090 32GBBudget pickMakes 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
Yes, RTX 5080 Laptop 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: 24.3 tok/s.
OLMo 2 32B (32B parameters) requires approximately 26.2 GB at Q4_K_M quantization. On RTX 5080 Laptop 16GB, it fits at Q2_K using 19.2 GB.
The recommended quantization is Q4_K_M, but on RTX 5080 Laptop 16GB the best fitting quantization is Q2_K, which uses 19.2 GB.
On RTX 5080 Laptop 16GB, OLMo 2 32B achieves approximately 24.3 tokens per second decode speed with a time-to-first-token of 7973ms using Q2_K quantization.
For coding workloads, OLMo 2 32B on RTX 5080 Laptop 16GB receives a F grade with 9.5 tok/s and 4K context.
On RTX 5080 Laptop 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/olmo-2-32b-on-rtx-5080-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.