Llama 3.1 70B needs ~53.3 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M 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
5.3 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~4.2 GB host RAM)
Decode
8.9 tok/s
TTFT
21633 ms
Safe context
4K
Memory
53.3 GB / 48.0 GB
Offload
10%
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 4.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~2.4 GB host RAM) | 9.9 tok/s | 10691 ms | 4K |
| Coding | B | Very compromised (needs ~4.2 GB host RAM) | 8.9 tok/s | 21633 ms | 4K |
| Agentic Coding | F | Too heavy | 7.4 tok/s | 37845 ms | 4K |
| Reasoning | B | Very compromised (needs ~4.2 GB host RAM) | 8.9 tok/s | 25567 ms | 4K |
| RAG | F | Too heavy | 7.4 tok/s | 47306 ms | 4K |
How Llama 3.1 70B (70B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | A79 |
Q3_K_SBest for your GPU | 3 | 34.3 GB | Low | A79 |
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 3.1 70B on your machine.
Run
ollama run llama3.1Opciones de mejora
NVIDIA A16 64GBOpción económicaElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 34%.
~$6,500 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBMejor relación calidad-precioElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 330%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBMejora NVIDIAElimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 284%.
~$9,999 MSRP
Yes, RTX A6000 48GB can run Llama 3.1 70B with a B grade (Very compromised (needs ~4.2 GB host RAM)). Expected decode speed: 8.9 tok/s.
Llama 3.1 70B (70B parameters) requires approximately 53.3 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 A6000 48GB, Llama 3.1 70B achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21633ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 70B on RTX A6000 48GB receives a B grade with 8.9 tok/s and 4K context.
On RTX A6000 48GB, 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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.1-70b-on-a6000-48gb" 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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