Raises estimated decode speed by about 223%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Gemma 2 9B needs ~14.2 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~39 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
39.0 tok/s
TTFT
4959 ms
Safe context
8K
Memory
14.2 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 | 39.0 tok/s | 2705 ms | 8K |
| Coding | B | Runs well | 39.0 tok/s | 4959 ms | 8K |
| Agentic Coding | B | Runs well | 39.0 tok/s | 7213 ms | 8K |
| Reasoning | B | Runs well | 39.0 tok/s | 5860 ms | 8K |
| RAG | B | Runs well | 39.0 tok/s | 9016 ms | 8K |
How Gemma 2 9B (9B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B59 |
Q3_K_S | 3 | 4.4 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 2 9B on your machine.
Run
ollama run gemma2Upgrade options
Raises estimated decode speed by about 223%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 223%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, Tesla P40 24GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 39.0 tok/s.
Gemma 2 9B (9B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.
On Tesla P40 24GB, Gemma 2 9B achieves approximately 39.0 tokens per second decode speed with a time-to-first-token of 4959ms using Q4_K_M quantization.
For coding workloads, Gemma 2 9B on Tesla P40 24GB receives a B grade with 39.0 tok/s and 8K context.
On Tesla P40 24GB, Gemma 2 9B 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/gemma-2-9b-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:
5.0 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 5.5 GB | Medium | B60 |
Q5_K_M | 5 | 6.5 GB | High | B61 |
Q6_K | 6 | 7.4 GB | High | B61 |
Q8_0 | 8 | 9.6 GB | Very High | B63 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B64 |