Llama 3.3 70B Instruct needs ~54.5 GB but RTX 3090 Ti 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
30.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.5 tok/s
TTFT
77006 ms
Safe context
4K
Memory
54.5 GB / 24.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 54.5 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 | 40051 ms | 4K |
| Coding | F | Too heavy | 2.5 tok/s | 77006 ms | 4K |
| Agentic Coding | F | Too heavy | 2.5 tok/s | 112008 ms | 4K |
| Reasoning | F | Too heavy | 2.5 tok/s | 91007 ms | 4K |
| RAG | F | Too heavy | 2.5 tok/s | 140010 ms | 4K |
How Llama 3.3 70B Instruct (70B params) fits at each quantization level on RTX 3090 Ti 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 |
アップグレードオプション
RTX A6000 48GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 188%.
〜$4,650 MSRP
RTX PRO 5000 Blackwell 48GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 472%.
〜$4,999 MSRP
NVIDIA A16 64GBコスパ重視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最大の飛躍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 Instruct requires more memory than RTX 3090 Ti 24GB provides.
Llama 3.3 70B Instruct (70B parameters) requires approximately 54.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RTX 3090 Ti 24GB, Llama 3.3 70B Instruct achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 77006ms using Q4_K_M quantization.
For coding workloads, Llama 3.3 70B Instruct on RTX 3090 Ti 24GB receives a F grade with 2.5 tok/s and 4K context.
On RTX 3090 Ti 24GB, Llama 3.3 70B Instruct can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-maziyarpanahi--llama-3-3-70b-instruct-gguf-on-rtx-3090-ti-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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