Baichuan 13B needs ~35.3 GB VRAM. AMD Instinct MI250 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
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 | 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 |
Inference speed
Estimated decode speed (tokens/sec) for Baichuan 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| 32 GB | Q5_K_M | 130.8 | Fits | |
| 24 GB | Q5_K_M | 61.0 | Offloads | |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 52.4 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.6 | Fits |
| 24 GB | Q5_K_M | 48.7 | Offloads | |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 24.0 | Fits |
| 16 GB | Q5_K_M | 20.6 | Too big | |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.2 | Fits |
| 12 GB | Q5_K_M | 7.2 | Too big | |
| 12 GB | Q5_K_M | 4.5 | Too big | |
| 8 GB | Q5_K_M | 3.2 | Too big |
Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Baichuan 13B (13B params) fits at each quantization level on AMD Instinct MI250 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 | 7.3 GB | 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 |
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, AMD Instinct MI250 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 AMD Instinct MI250 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 AMD Instinct MI250 128GB receives a B grade with 182.0 tok/s and 8K context.
On AMD Instinct MI250 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/baichuan-13b-on-instinct-mi250-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: