Baichuan 13B needs ~23.0 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~34 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
4.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
25.7 tok/s
TTFT
7544 ms
Safe context
8K
Memory
24.5 GB / 20.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% 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 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 52.3 tok/s | 2019 ms | 8K |
| Coding | F | Too heavy | 25.7 tok/s | 7544 ms | 8K |
| Agentic Coding | F | Too heavy | 10.9 tok/s | 25723 ms | 8K |
| Reasoning | F | Too heavy | 25.7 tok/s | 8915 ms | 8K |
| RAG | F | Too heavy | 10.9 tok/s | 32153 ms | 8K |
How Baichuan 13B (13B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B63 |
Q3_K_S | 3 | 6.4 GB | Low | B64 |
NVFP4 | 4 | 7.3 GB | Medium | B65 |
Q4_K_M | 4 | 7.9 GB | Medium | B65 |
Q5_K_M | 5 | 9.4 GB | High | B66 |
Q6_K | 6 | 10.7 GB | High | B67 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | B66 |
F16 | 16 | 26.7 GB | Maximum | F0 |
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 99Opciones de mejora
Hace 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.
~$999 MSRP
Hace 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.
~$1,899 MSRP
Hace 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.
~$2,249 MSRP
Yes, RX 7900 XT 20GB can run Baichuan 13B at Q4_K_M quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q5_K_M requires 24.5 GB which exceeds available memory, but at Q4_K_M it needs only 23.0 GB. Expected decode speed: 33.7 tok/s.
Baichuan 13B (13B parameters) requires approximately 24.5 GB at Q5_K_M quantization. On RX 7900 XT 20GB, it fits at Q4_K_M using 23.0 GB.
The recommended quantization is Q5_K_M, but on RX 7900 XT 20GB the best fitting quantization is Q4_K_M, which uses 23.0 GB.
On RX 7900 XT 20GB, Baichuan 13B achieves approximately 33.7 tokens per second decode speed with a time-to-first-token of 5743ms using Q4_K_M quantization.
For coding workloads, Baichuan 13B on RX 7900 XT 20GB receives a F grade with 25.7 tok/s and 8K context.
On RX 7900 XT 20GB, Baichuan 13B can safely use up to 8K tokens of context at Q4_K_M quantization. The model's official context limit is 8K, 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/baichuan-13b-on-rx-7900-xt-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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