baichuan inc Baichuan M2 32B needs ~19.0 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q2_K quantization, expect ~8 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
10.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.0 tok/s
TTFT
64331 ms
Safe context
4K
Memory
26.1 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 2.0 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 | 3.5 tok/s | 29988 ms | 4K |
| Coding | F | Too heavy | 3.0 tok/s | 64331 ms | 4K |
| Agentic Coding | F | Too heavy | 2.3 tok/s | 124168 ms | 4K |
| Reasoning | F | Too heavy | 3.0 tok/s | 76028 ms | 4K |
| RAG | F | Too heavy | 2.3 tok/s | 155211 ms | 4K |
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on RTX 2000 Ada 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 | 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 |
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 startアップグレードオプション
RTX 3090 24GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 560%.
〜$1,499 MSRP
RTX 4090 24GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 673%.
〜$1,599 MSRP
RTX 5090 32GBコスパ重視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.
〜$1,999 MSRP
Yes, RTX 2000 Ada 16GB can run baichuan inc Baichuan M2 32B at Q2_K quantization (Very compromised (needs ~2 GB host RAM)). The recommended Q4_K_M requires 26.1 GB which exceeds available memory, but at Q2_K it needs only 19.0 GB. Expected decode speed: 7.8 tok/s.
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 26.1 GB at Q4_K_M quantization. On RTX 2000 Ada 16GB, it fits at Q2_K using 19.0 GB.
The recommended quantization is Q4_K_M, but on RTX 2000 Ada 16GB the best fitting quantization is Q2_K, which uses 19.0 GB.
On RTX 2000 Ada 16GB, baichuan inc Baichuan M2 32B achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24945ms using Q2_K quantization.
For coding workloads, baichuan inc Baichuan M2 32B on RTX 2000 Ada 16GB receives a F grade with 3.0 tok/s and 4K context.
On RTX 2000 Ada 16GB, baichuan inc Baichuan M2 32B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
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.
Paste this snippet into any page to show a live fit card.
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