Radeon AI PRO R9700 32GBBudget pick
32 GB VRAM (+8)
A
Adds memory headroom for longer context windows and future model growth.41.1 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Baichuan 13B needs ~24.9 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q5_K_M quantization, expect ~52 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
0.9 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
52.4 tok/s
TTFT
3693 ms
Safe context
8K
Memory
24.9 GB / 24.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 75.3 tok/s | 1402 ms | 8K |
| Coding | B | Runs with offload | 52.4 tok/s | 3693 ms | 8K |
| Agentic Coding | F | Too heavy | 22.6 tok/s | 12451 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 52.4 tok/s | 4364 ms | 8K |
| RAG | F | Too heavy | 22.6 tok/s | 15563 ms | 8K |
How Baichuan 13B (13B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B62 |
Q3_K_S | 3 | 6.4 GB | Low | B62 |
NVFP4 | 4 |
Copy-paste commands to run Baichuan 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-13B-Chat" \
--hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,249 MSRP
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, RX 7900 XTX 24GB can run Baichuan 13B with a B grade (Runs with offload). Expected decode speed: 52.4 tok/s.
Baichuan 13B (13B parameters) requires approximately 24.9 GB of memory with Q5_K_M quantization.
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
On RX 7900 XTX 24GB, Baichuan 13B achieves approximately 52.4 tokens per second decode speed with a time-to-first-token of 3693ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on RX 7900 XTX 24GB receives a B grade with 52.4 tok/s and 8K context.
On RX 7900 XTX 24GB, Baichuan 13B 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-13b-on-rx-7900-xtx-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.3 GB |
| Medium |
| B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B64 |
Q6_K | 6 | 10.7 GB | High | B65 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.