Llama 3.1 70B needs ~53.3 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~8 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
7.8 tok/s
TTFT
24770 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) | 8.6 tok/s | 12241 ms | 4K |
| Coding | B | Very compromised (needs ~4.2 GB host RAM) | 7.8 tok/s | 24770 ms | 4K |
| Agentic Coding | F | Too heavy | 6.5 tok/s | 43333 ms | 4K |
| Reasoning | B | Very compromised (needs ~4.2 GB host RAM) | 7.8 tok/s | 29274 ms | 4K |
| RAG | F | Too heavy | 6.5 tok/s | 54166 ms |
How Llama 3.1 70B (70B params) fits at each quantization level on Radeon Pro W7900 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 |
Copy-paste commands to run Llama 3.1 70B on your machine.
Run
ollama run llama3.1Upgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1806%.
~$8,000 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 264%.
~$10,000 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1112%.
~$12,000 MSRP
Yes, Radeon Pro W7900 48GB can run Llama 3.1 70B with a B grade (Very compromised (needs ~4.2 GB host RAM)). Expected decode speed: 7.8 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 Radeon Pro W7900 48GB, Llama 3.1 70B achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24770ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 70B on Radeon Pro W7900 48GB receives a B grade with 7.8 tok/s and 4K context.
On Radeon Pro W7900 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.1-70b-on-radeon-pro-w7900-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 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 |
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.