RTX 5090 32GBBudget pick32 GB VRAM (+8)1792 GB/s (+1446)
B
Raises estimated decode speed by about 91%.98 tok/s decode
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Mistral 7B Instruct v0.3 needs ~9.8 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
51.4 tok/s
TTFT
3767 ms
Safe context
8K
Memory
9.8 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 | 51.4 tok/s | 2055 ms | 8K |
| Coding | B | Runs well | 47.8 tok/s | 4050 ms | 8K |
| Agentic Coding | B | Runs well | 51.4 tok/s | 5479 ms | 8K |
| Reasoning | B | Runs well | 51.4 tok/s | 4452 ms | 8K |
| RAG | B | Runs well | 51.4 tok/s | 6849 ms | 8K |
How Mistral 7B Instruct v0.3 (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 | B57 |
Q3_K_S | 3 | 3.4 GB | Low | B57 |
NVFP4 | 4 |
Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.
Run
lms load Mistral-7B-Instruct-v0.3 && lms server startUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 91%.
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 91%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRaises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, Tesla P40 24GB can run Mistral 7B Instruct v0.3 with a B grade (Runs well). Expected decode speed: 47.8 tok/s.
Mistral 7B Instruct v0.3 (7B parameters) requires approximately 9.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral 7B Instruct v0.3 is Q4_K_M, which balances quality and memory efficiency.
On Tesla P40 24GB, Mistral 7B Instruct v0.3 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, Mistral 7B Instruct v0.3 on Tesla P40 24GB receives a B grade with 47.8 tok/s and 8K context.
On Tesla P40 24GB, Mistral 7B Instruct v0.3 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/mistral-7b-instruct-v0.3-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:
3.9 GB |
| Medium |
| B57 |
Q4_K_M | 4 | 4.3 GB | Medium | B57 |
Q5_K_M | 5 | 5.0 GB | High | B58 |
Q6_K | 6 | 5.7 GB | High | B58 |
Q8_0 | 8 | 7.5 GB | Very High | B59 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B62 |