baichuan inc Baichuan M2 32B needs ~19.0 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q2_K quantization, expect ~22 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
8.4 tok/s
TTFT
23050 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 | 9.8 tok/s | 10745 ms | 4K |
| Coding | F | Too heavy | 8.4 tok/s | 23050 ms | 4K |
| Agentic Coding | F | Too heavy | 6.3 tok/s | 44490 ms | 4K |
| Reasoning | F | Too heavy | 8.4 tok/s | 27241 ms | 4K |
| RAG | F | Too heavy | 6.3 tok/s | 55612 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for baichuan inc Baichuan M2 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~62 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 61.5 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 30.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 30.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 28.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 23.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 23.2 | Heavy offload |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 22.5 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 21.4 | Heavy offload |
| RTX 3090 24GB | 24 GB | Q4_K_M | 19.8 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 19.4 | Tight |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 12.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 11.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 8.4 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.9 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on RTX 4080 Super 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 136%.
〜$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 176%.
〜$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 4080 Super 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: 21.7 tok/s.
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 26.1 GB at Q4_K_M quantization. On RTX 4080 Super 16GB, it fits at Q2_K using 19.0 GB.
The recommended quantization is Q4_K_M, but on RTX 4080 Super 16GB the best fitting quantization is Q2_K, which uses 19.0 GB.
On RTX 4080 Super 16GB, baichuan inc Baichuan M2 32B achieves approximately 21.7 tokens per second decode speed with a time-to-first-token of 8938ms using Q2_K quantization.
For coding workloads, baichuan inc Baichuan M2 32B on RTX 4080 Super 16GB receives a F grade with 8.4 tok/s and 4K context.
On RTX 4080 Super 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.
<iframe src="https://willitrunai.com/embed/hf-bartowski--baichuan-inc-baichuan-m2-32b-gguf-on-rtx-4080-super-16gb" 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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