OLMo 2 32B needs ~25.5 GB but Radeon RX 6850M XT 12GB only has 12.0 GB. Try a smaller quantization or lighter model.
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
13.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
94463 ms
Safe context
4K
Memory
25.5 GB / 12.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 25.5 GB, but this setup only exposes 12.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.4 tok/s | 43576 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 94463 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140257 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 111638 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 175322 ms | 4K |
How OLMo 2 32B (32B params) fits at each quantization level on Radeon RX 6850M XT 12GB (12.0 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 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1045%.
~$999 MSRP
Makes 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.
~$1,899 MSRP
Makes 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.
~$2,249 MSRP
NVIDIA A100 40GBBiggest leapMakes 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.
~$10,000 MSRP
No, OLMo 2 32B requires more memory than Radeon RX 6850M XT 12GB provides.
OLMo 2 32B (32B parameters) requires approximately 25.5 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 Radeon RX 6850M XT 12GB, OLMo 2 32B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 94463ms using Q4_K_M quantization.
For coding workloads, OLMo 2 32B on Radeon RX 6850M XT 12GB receives a F grade with 2.0 tok/s and 4K context.
On Radeon RX 6850M XT 12GB, 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-rx-6850m-xt-12gb" 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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
