Baichuan M2 32B Q4 K M needs ~25.3 GB but RTX 3070 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
17.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.4 tok/s
TTFT
80337 ms
Safe context
4K
Memory
25.3 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 25.3 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.4 tok/s | 43820 ms | 4K |
| Coding | F | Too heavy | 2.4 tok/s | 80337 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116854 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 94944 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 146068 ms | 4K |
How Baichuan M2 32B Q4 K M (32B params) fits at each quantization level on RTX 3070 8GB (8.0 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 |
Opciones de mejora
RTX 3090 24GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 725%.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 867%.
~$1,599 MSRP
RTX 5090 32GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,999 MSRP
No, Baichuan M2 32B Q4 K M requires more memory than RTX 3070 8GB provides.
Baichuan M2 32B Q4 K M (32B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan M2 32B Q4 K M is Q4_K_M, which balances quality and memory efficiency.
On RTX 3070 8GB, Baichuan M2 32B Q4 K M achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 80337ms using Q4_K_M quantization.
For coding workloads, Baichuan M2 32B Q4 K M on RTX 3070 8GB receives a F grade with 2.4 tok/s and 4K context.
On RTX 3070 8GB, Baichuan M2 32B Q4 K M can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-baichuan-inc--baichuan-m2-32b-q4-k-m-gguf-on-rtx-3070-8gb" 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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