Llama 3.1 405B needs ~284.4 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~24 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
Fit status
Runs with offload
Decode
25.9 tok/s
TTFT
7488 ms
Safe context
23K
Memory
284.4 GB / 288.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload | 25.9 tok/s | 4084 ms | 23K |
| Coding | A | Runs with offload | 23.6 tok/s | 8190 ms | 23K |
| Agentic Coding | A | Runs with offload | 17.2 tok/s | 16367 ms | 23K |
| Reasoning | A | Runs with offload | 25.9 tok/s | 8849 ms | 23K |
| RAG | A | Runs with offload (needs ~3.5 GB host RAM) | 18.8 tok/s | 18705 ms |
How Llama 3.1 405B (405B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 158.0 GB | Low | A82 |
Q3_K_S | 3 | 198.5 GB | Low | A82 |
NVFP4Best for your GPU |
Copy-paste commands to run Llama 3.1 405B on your machine.
Run
ollama run llama3.1:405bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 480B | A | 35.3 tok/s |
Yes, AMD Instinct MI350X 288GB can run Llama 3.1 405B with a A grade (Runs with offload). Expected decode speed: 23.6 tok/s.
Llama 3.1 405B (405B parameters) requires approximately 284.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.1 405B is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI350X 288GB, Llama 3.1 405B achieves approximately 23.6 tokens per second decode speed with a time-to-first-token of 8190ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 405B on AMD Instinct MI350X 288GB receives a A grade with 23.6 tok/s and 23K context.
On AMD Instinct MI350X 288GB, Llama 3.1 405B can safely use up to 23K tokens of context. The model's official context limit is 131K, 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-3.1-405b-on-instinct-mi350x-288gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 23K |
| 4 |
226.8 GB |
| Medium |
| A82 |
Q4_K_M | 4 | 247.1 GB | Medium | F0 |
Q5_K_M | 5 | 291.6 GB | High | F0 |
Q6_K | 6 | 332.1 GB | High | F0 |
Q8_0 | 8 | 433.4 GB | Very High | F0 |
F16 | 16 | 830.2 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.