baichuan inc Baichuan M2 32B needs ~28.5 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~67 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
Fit status
Runs well
Decode
66.9 tok/s
TTFT
2893 ms
Safe context
65K
Memory
28.5 GB / 40.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 66.9 tok/s | 1578 ms | 65K |
| Coding | B | Runs well | 66.9 tok/s | 2893 ms | 65K |
| Agentic Coding | B | Runs well | 66.9 tok/s | 4208 ms | 65K |
| Reasoning | B | Runs well | 66.9 tok/s | 3419 ms | 65K |
| RAG | B | Runs well | 66.9 tok/s | 5260 ms | 65K |
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C45 |
Q3_K_S | 3 | 15.7 GB | Low | C46 |
NVFP4 | 4 | 17.9 GB | Medium | C47 |
Q4_K_M | 4 | 19.5 GB | Medium | C48 |
Q5_K_M | 5 | 23.0 GB | High | C48 |
Q6_KBest for your GPU | 6 | 26.2 GB | High | C48 |
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 startYes, NVIDIA A100 40GB can run baichuan inc Baichuan M2 32B with a B grade (Runs well). Expected decode speed: 66.9 tok/s.
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 28.5 GB of memory with Q4_K_M quantization.
The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A100 40GB, baichuan inc Baichuan M2 32B achieves approximately 66.9 tokens per second decode speed with a time-to-first-token of 2893ms using Q4_K_M quantization.
For coding workloads, baichuan inc Baichuan M2 32B on NVIDIA A100 40GB receives a B grade with 66.9 tok/s and 65K context.
On NVIDIA A100 40GB, baichuan inc Baichuan M2 32B can safely use up to 65K 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-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-a100-40gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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