Llama 3.3 70B needs ~51.2 GB but RTX A5500 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
2.3 tok/s
TTFT
83320 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 | 2.6 tok/s | 41004 ms | 4K |
| Coding | F | Too heavy | 2.3 tok/s | 83320 ms | 4K |
| Agentic Coding | F | Too heavy | 2.3 tok/s | 123051 ms | 4K |
| Reasoning | F | Too heavy | 2.3 tok/s | 98469 ms | 4K |
| RAG | F | Too heavy | 2.3 tok/s | 153813 ms | 4K |
How Llama 3.3 70B (70B params) fits at each quantization level on RTX A5500 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 | 39.2 GB | 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 |
升级选项
RTX A6000 48GB最佳性价比Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 283%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIA 升级Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 665%.
~$4,999 MSRP
NVIDIA A16 64GB经济之选Makes 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 80GB最大飞跃Makes 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 3.3 70B requires more memory than RTX A5500 24GB provides.
Llama 3.3 70B (70B parameters) requires approximately 51.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.
On RTX A5500 24GB, Llama 3.3 70B achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 83320ms using Q4_K_M quantization.
For coding workloads, Llama 3.3 70B on RTX A5500 24GB receives a F grade with 2.3 tok/s and 4K context.
On RTX A5500 24GB, Llama 3.3 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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.3-70b-on-rtx-a5500-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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