Baichuan 7B needs ~15.3 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~66 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
65.8 tok/s
TTFT
2944 ms
Safe context
8K
Memory
15.3 GB / 20.0 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 | B | Runs well | 65.8 tok/s | 1606 ms | 8K |
| Coding | A | Runs well | 65.8 tok/s | 2944 ms | 8K |
| Agentic Coding | B | Very compromised (needs ~0.6 GB host RAM) | 36.4 tok/s | 7729 ms | 8K |
| Reasoning | A | Runs well | 65.8 tok/s | 3479 ms | 8K |
| RAG | B | Very compromised (needs ~0.6 GB host RAM) | 36.4 tok/s | 9662 ms | 8K |
How Baichuan 7B (7B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B61 |
Q3_K_S | 3 | 3.4 GB | Low | B62 |
NVFP4 | 4 | 3.9 GB | Medium | B62 |
Q4_K_M | 4 | 4.3 GB | Medium | B62 |
Q5_K_M | 5 | 5.0 GB | High | B63 |
Q6_K | 6 | 5.7 GB | High | B63 |
Q8_0 | 8 | 7.5 GB | Very High | B65 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B66 |
Copy-paste commands to run Baichuan 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-7B" \
--hf-file "Baichuan-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | A | 23.2 tok/s | |
| Qwen 3.5 27B | 27B | A | 10.4 tok/s | |
| Qwen 3.6 27B | 27B | S | 13 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | A | 24.6 tok/s | |
| Qwen 3.5 9B | 9B | S | 55 tok/s |
Yes, RTX 4000 Ada 20GB can run Baichuan 7B with a A grade (Runs well). Expected decode speed: 65.8 tok/s.
Baichuan 7B (7B parameters) requires approximately 15.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada 20GB, Baichuan 7B achieves approximately 65.8 tokens per second decode speed with a time-to-first-token of 2944ms using Q4_K_M quantization.
For coding workloads, Baichuan 7B on RTX 4000 Ada 20GB receives a A grade with 65.8 tok/s and 8K context.
On RTX 4000 Ada 20GB, Baichuan 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/baichuan-7b-on-rtx-4000-ada-20gb" 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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