RTX PRO 5000 Blackwell 48GB经济之选48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.57.8 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Can baichuan inc Baichuan M2 32B run on RTX 5090 32GB?
YES — Tight Fit
C52Usable
Estimated from fit model
baichuan inc Baichuan M2 32B needs ~27.7 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~62 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 27.7 GB, 61.5 tok/s, Tight fit
27.7 GB required32.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
61.5 tok/s
TTFT
3148 ms
Safe context
34K
Memory
27.7 GB / 32.0 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsbaichuan inc Baichuan M2 32B on RTX 5090 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 61.5 tok/s decode · 3.1s TTFT (warm) · 154 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 61.5 tok/s | 1717 ms | 34K |
| Coding | C | Tight fit | 61.5 tok/s | 3148 ms | 34K |
| Agentic Coding | C | Runs with offload | 61.5 tok/s | 4578 ms | 34K |
| Reasoning | C | Tight fit | 61.5 tok/s | 3720 ms | 34K |
| RAG | C | Runs with offload | 61.5 tok/s | 5723 ms | 34K |
Quantization options
How baichuan inc Baichuan M2 32B (32B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C47 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4 | 4 | 17.9 GB | Medium | C49 |
Q4_K_M | 4 | 19.5 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 23.0 GB | High | C48 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Get started
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升级选项
能流畅运行 baichuan inc Baichuan M2 32B 的硬件
Frequently asked questions
Can RTX 5090 32GB run baichuan inc Baichuan M2 32B?
Yes, RTX 5090 32GB can run baichuan inc Baichuan M2 32B with a C grade (Tight fit). Expected decode speed: 61.5 tok/s.
How much VRAM does baichuan inc Baichuan M2 32B need?
baichuan inc Baichuan M2 32B (32B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.
What is the best quantization for baichuan inc Baichuan M2 32B?
The recommended quantization for baichuan inc Baichuan M2 32B is Q4_K_M, which balances quality and memory efficiency.
What speed will baichuan inc Baichuan M2 32B run at on RTX 5090 32GB?
On RTX 5090 32GB, baichuan inc Baichuan M2 32B achieves approximately 61.5 tokens per second decode speed with a time-to-first-token of 3148ms using Q4_K_M quantization.
Can RTX 5090 32GB run baichuan inc Baichuan M2 32B for coding?
For coding workloads, baichuan inc Baichuan M2 32B on RTX 5090 32GB receives a C grade with 61.5 tok/s and 34K context.
What context window can baichuan inc Baichuan M2 32B use on RTX 5090 32GB?
On RTX 5090 32GB, baichuan inc Baichuan M2 32B can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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