RTX 5090 32GBBudget pick32 GB VRAM (+8)1792 GB/s (+1446)
B
Raises estimated decode speed by about 87%.98 tok/s decode
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Falcon 7B Instruct needs ~8.0 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 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
52.5 tok/s
TTFT
3686 ms
Safe context
8K
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 | B | Runs well | 52.5 tok/s | 2011 ms | 8K |
| Coding | B | Runs well | 52.5 tok/s | 3686 ms | 8K |
| Agentic Coding | B | Runs well | 52.5 tok/s | 5362 ms | 8K |
| Reasoning | B | Runs well | 52.5 tok/s | 4356 ms | 8K |
| RAG | B | Runs well | 52.5 tok/s | 6702 ms | 8K |
How Falcon 7B Instruct (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 |
Copy-paste commands to run Falcon 7B Instruct on your machine.
Run
lms load falcon-7b-instruct && lms server startUpgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 87%.
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 87%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRaises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Yes, Tesla P40 24GB can run Falcon 7B Instruct with a B grade (Runs well). Expected decode speed: 52.5 tok/s.
Falcon 7B Instruct (7B parameters) requires approximately 8.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Falcon 7B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Tesla P40 24GB, Falcon 7B Instruct achieves approximately 52.5 tokens per second decode speed with a time-to-first-token of 3686ms using Q4_K_M quantization.
For coding workloads, Falcon 7B Instruct on Tesla P40 24GB receives a B grade with 52.5 tok/s and 8K context.
On Tesla P40 24GB, Falcon 7B Instruct 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/falcon-7b-instruct-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 |
| 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 |