RTX 5090 32GBBudget pick32 GB VRAM (+8)1792 GB/s (+1446)
C
Raises estimated decode speed by about 51%.84 tok/s decode
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yi 1.5 6B Chat needs ~8.0 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~56 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
55.8 tok/s
TTFT
3471 ms
Safe context
381K
Memory
8.0 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 | C | Runs well | 55.8 tok/s | 1893 ms | 381K |
| Coding | C | Runs well | 55.8 tok/s | 3471 ms | 381K |
| Agentic Coding | C | Runs well | 55.8 tok/s | 5049 ms | 381K |
| Reasoning | C | Runs well | 55.8 tok/s | 4102 ms | 381K |
| RAG | C | Runs well | 55.8 tok/s | 6311 ms | 381K |
How Yi 1.5 6B Chat (6B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C44 |
Q3_K_S | 3 | 2.9 GB | Low | C44 |
NVFP4 | 4 | 3.4 GB | Medium | C45 |
Q4_K_M | 4 | 3.7 GB | Medium | C45 |
Q5_K_M | 5 | 4.3 GB | High | C45 |
Q6_K | 6 | 4.9 GB | High | C45 |
Q8_0 | 8 | 6.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C50 |
Copy-paste commands to run Yi 1.5 6B Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-1-5-6b-chat-gguf && lms server startUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRaises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRaises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, Tesla P40 24GB can run Yi 1.5 6B Chat with a C grade (Runs well). Expected decode speed: 55.8 tok/s.
Yi 1.5 6B Chat (6B parameters) requires approximately 8.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 6B Chat is Q4_K_M, which balances quality and memory efficiency.
On Tesla P40 24GB, Yi 1.5 6B Chat achieves approximately 55.8 tokens per second decode speed with a time-to-first-token of 3471ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 6B Chat on Tesla P40 24GB receives a C grade with 55.8 tok/s and 381K context.
On Tesla P40 24GB, Yi 1.5 6B Chat can safely use up to 381K tokens of context. The model's official context limit is —, 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/hf-maziyarpanahi--yi-1-5-6b-chat-gguf-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: