Baichuan 13B needs ~35.3 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q5_K_M quantization, expect ~182 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
182.0 tok/s
TTFT
1064 ms
Safe context
8K
Memory
35.3 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 182.0 tok/s | 580 ms | 8K |
| Coding | B | Runs well | 182.0 tok/s | 1064 ms | 8K |
| Agentic Coding | B | Runs well | 182.0 tok/s | 1547 ms | 8K |
| Reasoning | B | Runs well | 182.0 tok/s | 1257 ms | 8K |
| RAG | B | Runs well | 182.0 tok/s | 1934 ms | 8K |
How Baichuan 13B (13B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | C54 |
Q3_K_S | 3 | 6.4 GB | Low | C54 |
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 99Yes, Gaudi 3 128GB can run Baichuan 13B with a B grade (Runs well). Expected decode speed: 182.0 tok/s.
Baichuan 13B (13B parameters) requires approximately 35.3 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 Gaudi 3 128GB, Baichuan 13B achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on Gaudi 3 128GB receives a B grade with 182.0 tok/s and 8K context.
On Gaudi 3 128GB, 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.
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/baichuan-13b-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C54 |
Q4_K_M | 4 | 7.9 GB | Medium | C54 |
Q5_K_M | 5 | 9.4 GB | High | C54 |
Q6_K | 6 | 10.7 GB | High | C54 |
Q8_0 | 8 | 13.9 GB | Very High | C55 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B56 |
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.