Llama 3.1 70B needs ~51.2 GB but RTX 5090 Laptop 24GB only has 24.0 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
27.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.9 tok/s
TTFT
66316 ms
Safe context
4K
Memory
51.2 GB / 24.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 51.2 GB, but this setup only exposes 24.0 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 | 3.2 tok/s | 32636 ms | 4K |
| Coding | F | Too heavy | 2.9 tok/s | 66316 ms | 4K |
| Agentic Coding | F | Too heavy | 2.9 tok/s | 97938 ms | 4K |
| Reasoning | F | Too heavy | 2.9 tok/s | 78373 ms | 4K |
| RAG | F | Too heavy | 2.9 tok/s | 122423 ms | 4K |
How Llama 3.1 70B (70B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | F0 |
Q3_K_S | 3 | 34.3 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX A6000 48GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 203%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 507%.
~$4,999 MSRP
NVIDIA A16 64GBBudget 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.
~$6,500 MSRP
NVIDIA H100 80GBBiggest leapMakes 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.
~$40,000 MSRP
No, Llama 3.1 70B requires more memory than RTX 5090 Laptop 24GB provides.
Llama 3.1 70B (70B parameters) requires approximately 51.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.1 70B is Q4_K_M, which balances quality and memory efficiency.
On RTX 5090 Laptop 24GB, Llama 3.1 70B achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 66316ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 70B on RTX 5090 Laptop 24GB receives a F grade with 2.9 tok/s and 4K context.
On RTX 5090 Laptop 24GB, Llama 3.1 70B can safely use up to 4K tokens of context. The model's official context limit is 128K, 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/llama-3.1-70b-on-rtx-5090-laptop-24gb" 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 | 42.7 GB | Medium | F0 |
Q5_K_M | 5 | 50.4 GB | High | F0 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 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.
