Baichuan 13B needs ~23.3 GB VRAM. RTX A4500 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
4.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
26.0 tok/s
TTFT
7442 ms
Safe context
8K
Memory
24.8 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 1.1 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 | 54.4 tok/s | 1941 ms | 8K |
| Coding | F | Too heavy | 26.0 tok/s | 7442 ms | 8K |
| Agentic Coding | F | Too heavy | 11.2 tok/s | 25161 ms | 8K |
| Reasoning | F | Too heavy | 26.0 tok/s | 8795 ms | 8K |
| RAG | F | Too heavy | 11.2 tok/s | 31451 ms | 8K |
How Baichuan 13B (13B params) fits at each quantization level on RTX A4500 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 99Upgrade-Optionen
RTX 3090 24GBGünstige WahlMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $1,499 MSRP
RTX 4090 24GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $1,599 MSRP
RTX A5500 24GBNVIDIA-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $3,200 MSRP
Yes, RTX A4500 20GB can run Baichuan 13B at Q4_K_M quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q5_K_M requires 24.8 GB which exceeds available memory, but at Q4_K_M it needs only 23.3 GB. Expected decode speed: 34.1 tok/s.
Baichuan 13B (13B parameters) requires approximately 24.8 GB at Q5_K_M quantization. On RTX A4500 20GB, it fits at Q4_K_M using 23.3 GB.
The recommended quantization is Q5_K_M, but on RTX A4500 20GB the best fitting quantization is Q4_K_M, which uses 23.3 GB.
On RTX A4500 20GB, Baichuan 13B achieves approximately 34.1 tokens per second decode speed with a time-to-first-token of 5674ms using Q4_K_M quantization.
For coding workloads, Baichuan 13B on RTX A4500 20GB receives a F grade with 26.0 tok/s and 8K context.
On RTX A4500 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-rtx-a4500-20gb" 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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