baichuan inc Baichuan M2 32B needs ~26.6 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~6 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
2.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.9 GB host RAM)
Decode
6.1 tok/s
TTFT
31732 ms
Safe context
5K
Memory
26.6 GB / 24.0 GB
Offload
10%
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.5 GB host RAM) | 7.2 tok/s | 14739 ms | 5K |
| Coding | D | Very compromised (needs ~1.9 GB host RAM) | 6.1 tok/s | 31732 ms | 5K |
| Agentic Coding | F | Too heavy | 4.6 tok/s | 61680 ms | 5K |
| Reasoning | D | Very compromised (needs ~1.9 GB host RAM) | 6.1 tok/s | 37502 ms | 5K |
| RAG | F | Too heavy | 4.6 tok/s | 77100 ms | 5K |
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C50 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4Best for your GPU | 4 | 17.9 GB | Medium | C49 |
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 startOpções de upgrade
RTX 5090 32GBOpção econômicaRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 554%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBMelhor custo-benefícioRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 533%.
~$2,499 MSRP
RTX 5000 Ada 32GBUpgrade NVIDIARemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 287%.
~$4,000 MSRP
Yes, Tesla P40 24GB can run baichuan inc Baichuan M2 32B with a D grade (Very compromised (needs ~1.9 GB host RAM)). Expected decode speed: 6.1 tok/s.
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 26.6 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 Tesla P40 24GB, baichuan inc Baichuan M2 32B achieves approximately 6.1 tokens per second decode speed with a time-to-first-token of 31732ms using Q4_K_M quantization.
For coding workloads, baichuan inc Baichuan M2 32B on Tesla P40 24GB receives a D grade with 6.1 tok/s and 5K context.
On Tesla P40 24GB, baichuan inc Baichuan M2 32B can safely use up to 5K tokens of context. 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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