baichuan inc Baichuan M2 32B needs ~22.6 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q3_K_S quantization, expect ~10 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
6.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.0 tok/s
TTFT
32371 ms
Safe context
4K
Memory
26.5 GB / 20.0 GB
Offload
20%
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 1.8 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 | 7.0 tok/s | 15127 ms | 4K |
| Coding | F | Too heavy | 6.0 tok/s | 32371 ms | 4K |
| Agentic Coding | F | Too heavy | 4.5 tok/s | 62231 ms | 4K |
| Reasoning | F | Too heavy | 6.0 tok/s | 38257 ms | 4K |
| RAG | F | Too heavy | 4.5 tok/s | 77789 ms | 4K |
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 12.5 GB | Low | C50 |
Q3_K_S | 3 | 15.7 GB | Low | F0 |
Copy-paste commands to run baichuan inc Baichuan M2 32B on your machine.
Run
lms load hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf && 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 230%.
~$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 287%.
~$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 4000 Ada 20GB can run baichuan inc Baichuan M2 32B at Q3_K_S quantization (Very compromised (needs ~1.8 GB host RAM)). The recommended Q4_K_M requires 26.5 GB which exceeds available memory, but at Q3_K_S it needs only 22.6 GB. Expected decode speed: 9.6 tok/s.
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 26.5 GB at Q4_K_M quantization. On RTX 4000 Ada 20GB, it fits at Q3_K_S using 22.6 GB.
The recommended quantization is Q4_K_M, but on RTX 4000 Ada 20GB the best fitting quantization is Q3_K_S, which uses 22.6 GB.
On RTX 4000 Ada 20GB, baichuan inc Baichuan M2 32B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20103ms using Q3_K_S quantization.
For coding workloads, baichuan inc Baichuan M2 32B on RTX 4000 Ada 20GB receives a F grade with 6.0 tok/s and 4K context.
On RTX 4000 Ada 20GB, baichuan inc Baichuan M2 32B can safely use up to 5K tokens of context at Q3_K_S quantization. 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-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-rtx-4000-ada-20gb" 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 |
| 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.