Llama 3.3 70B needs ~36.6 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q2_K quantization, expect ~9 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
20.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.2 tok/s
TTFT
61077 ms
Safe context
4K
Memory
52.0 GB / 32.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 3.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.5 tok/s | 30106 ms | 4K |
| Coding | F | Too heavy | 3.2 tok/s | 61077 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 107319 ms | 4K |
| Reasoning | F | Too heavy | 3.2 tok/s | 72181 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 134148 ms | 4K |
How Llama 3.3 70B (70B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 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 |
Copy-paste commands to run Llama 3.3 70B on your machine.
Run
ollama run llama3.3Opciones de mejora
RTX A6000 48GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 175%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 450%.
~$4,999 MSRP
NVIDIA A16 64GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$6,500 MSRP
NVIDIA H100 80GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$40,000 MSRP
Yes, RTX 5000 Ada 32GB can run Llama 3.3 70B at Q2_K quantization (Very compromised (needs ~3.4 GB host RAM)). The recommended Q4_K_M requires 52.0 GB which exceeds available memory, but at Q2_K it needs only 36.6 GB. Expected decode speed: 8.8 tok/s.
Llama 3.3 70B (70B parameters) requires approximately 52.0 GB at Q4_K_M quantization. On RTX 5000 Ada 32GB, it fits at Q2_K using 36.6 GB.
The recommended quantization is Q4_K_M, but on RTX 5000 Ada 32GB the best fitting quantization is Q2_K, which uses 36.6 GB.
On RTX 5000 Ada 32GB, Llama 3.3 70B achieves approximately 8.8 tokens per second decode speed with a time-to-first-token of 21928ms using Q2_K quantization.
For coding workloads, Llama 3.3 70B on RTX 5000 Ada 32GB receives a F grade with 3.2 tok/s and 4K context.
On RTX 5000 Ada 32GB, Llama 3.3 70B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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<iframe src="https://willitrunai.com/embed/llama-3.3-70b-on-rtx-5000-ada-32gb" 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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