OLMo 2 32B needs ~26.7 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~23 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
2.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2 GB host RAM)
Decode
24.9 tok/s
TTFT
7775 ms
Safe context
4K
Memory
26.7 GB / 24.0 GB
Offload
10%
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.
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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 27.1 tok/s | 3904 ms | 4K |
| Coding | A | Very compromised | 23.1 tok/s | 8396 ms | 4K |
| Agentic Coding | F | Too heavy | 17.3 tok/s | 16275 ms | 4K |
| Reasoning | A | Very compromised | 23.1 tok/s | 9923 ms | 4K |
| RAG | F | Too heavy | 17.3 tok/s | 20344 ms | 4K |
How OLMo 2 32B (32B params) fits at each quantization level on RTX 5090 Laptop 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 |
Copy-paste commands to run OLMo 2 32B on your machine.
Run
lms load OLMo-2-0325-32B-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.6 35B A3B | 35B | A | 49 tok/s | |
|
Yes, RTX 5090 Laptop 24GB can run OLMo 2 32B with a A grade (Very compromised). Expected decode speed: 23.1 tok/s.
OLMo 2 32B (32B parameters) requires approximately 26.7 GB of memory with Q4_K_M quantization.
The recommended quantization for OLMo 2 32B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5090 Laptop 24GB, OLMo 2 32B achieves approximately 23.1 tokens per second decode speed with a time-to-first-token of 8396ms using Q4_K_M quantization.
For coding workloads, OLMo 2 32B on RTX 5090 Laptop 24GB receives a A grade with 23.1 tok/s and 4K context.
On RTX 5090 Laptop 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/olmo-2-32b-on-rtx-5090-laptop-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
| 35B |
| A |
| 65.3 tok/s |
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.