Baichuan M2 32B Q4 K M needs ~25.7 GB but RTX 3080 Ti 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
13.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.2 tok/s
TTFT
37005 ms
Safe context
4K
Memory
25.7 GB / 12.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 25.7 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 | 6.1 tok/s | 17206 ms | 4K |
| Coding | F | Too heavy | 5.2 tok/s | 37005 ms | 4K |
| Agentic Coding | F | Too heavy | 5.2 tok/s | 54299 ms | 4K |
| Reasoning | F | Too heavy | 5.2 tok/s | 43733 ms | 4K |
| RAG | F | Too heavy | 5.2 tok/s | 67874 ms | 4K |
How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on RTX 3080 Ti 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
RTX 3090 24GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 281%.
~$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 346%.
~$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
No, Baichuan M2 32B Q4 K M requires more memory than RTX 3080 Ti 12GB provides.
Baichuan M2 32B Q4 K M (32B parameters) requires approximately 25.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.
On RTX 3080 Ti 12GB, Baichuan M2 32B Q4 K M achieves approximately 5.2 tokens per second decode speed with a time-to-first-token of 37005ms using Q4_K_M quantization.
For coding workloads, Baichuan M2 32B Q4 K M on RTX 3080 Ti 12GB receives a F grade with 5.2 tok/s and 4K context.
On RTX 3080 Ti 12GB, Baichuan M2 32B Q4 K M can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf-on-rtx-3080-ti-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.
