Falcon 40B Instruct needs ~33.4 GB but RTX 4090 Laptop 16GB only has 16.0 GB. Try a smaller quantization or lighter model.
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
17.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.8 tok/s
TTFT
68605 ms
Safe context
4K
Memory
33.4 GB / 16.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 33.4 GB, but this setup only exposes 16.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.0 tok/s | 35297 ms | 4K |
| Coding | F | Too heavy | 2.8 tok/s | 68605 ms | 4K |
| Agentic Coding | F | Too heavy | 2.7 tok/s | 105777 ms | 4K |
| Reasoning | F | Too heavy | 2.8 tok/s | 81079 ms | 4K |
| RAG | F | Too heavy | 2.7 tok/s | 132221 ms | 4K |
How Falcon 40B Instruct (40B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 15.6 GB | Low | F0 |
Q3_K_S | 3 | 19.6 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX 5090 32GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 950%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 557%.
~$2,499 MSRP
RTX A6000 48GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$4,650 MSRP
No, Falcon 40B Instruct requires more memory than RTX 4090 Laptop 16GB provides.
Falcon 40B Instruct (40B parameters) requires approximately 33.4 GB of memory with Q5_K_M quantization.
The recommended quantization for Falcon 40B Instruct is Q5_K_M, which balances quality and memory efficiency.
On RTX 4090 Laptop 16GB, Falcon 40B Instruct achieves approximately 2.8 tokens per second decode speed with a time-to-first-token of 68605ms using Q5_K_M quantization.
For coding workloads, Falcon 40B Instruct on RTX 4090 Laptop 16GB receives a F grade with 2.8 tok/s and 4K context.
On RTX 4090 Laptop 16GB, Falcon 40B Instruct can safely use up to 4K 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-40b-instruct-on-rtx-4090-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 24.4 GB | Medium | F0 |
Q5_K_M | 5 | 28.8 GB | High | F0 |
Q6_K | 6 | 32.8 GB | High | F0 |
Q8_0 | 8 | 42.8 GB | Very High | F0 |
F16 | 16 | 82.0 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
