MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)410 GB/s (+64)
A
Raises estimated decode speed by about 38%.65.9 tok/s decode
Raises estimated decode speed by about 38%.
~$2,499 MSRP
Baichuan 7B needs ~15.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~48 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
47.8 tok/s
TTFT
4050 ms
Safe context
8K
Memory
15.7 GB / 24.0 GB
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 47.8 tok/s | 2209 ms | 8K |
| Coding | B | Runs well | 47.8 tok/s | 4050 ms | 8K |
| Agentic Coding | B | Runs with offload | 47.8 tok/s | 5890 ms | 8K |
| Reasoning | B | Runs well | 47.8 tok/s | 4786 ms | 8K |
| RAG | B | Runs with offload | 47.8 tok/s | 7363 ms | 8K |
How Baichuan 7B (7B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B60 |
Q3_K_S | 3 | 3.4 GB | Low | B61 |
NVFP4 | 4 | 3.9 GB | Medium | B61 |
Q4_K_M | 4 | 4.3 GB | Medium | B61 |
Q5_K_M | 5 | 5.0 GB | High | B62 |
Q6_K | 6 | 5.7 GB | High | B62 |
Q8_0 | 8 | 7.5 GB | Very High | B63 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B66 |
Copy-paste commands to run Baichuan 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-7B" \
--hf-file "Baichuan-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Yes, Tesla P40 24GB can run Baichuan 7B with a B grade (Runs well). Expected decode speed: 47.8 tok/s.
Baichuan 7B (7B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan 7B is Q4_K_M, which balances quality and memory efficiency.
On Tesla P40 24GB, Baichuan 7B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4050ms using Q4_K_M quantization.
For coding workloads, Baichuan 7B on Tesla P40 24GB receives a B grade with 47.8 tok/s and 8K context.
On Tesla P40 24GB, Baichuan 7B 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-7b-on-tesla-p40-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: