Qwen3-Coder 480B A35B Instruct needs ~325.4 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~35 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
37.4 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~33.6 GB host RAM)
Decode
35.3 tok/s
TTFT
5482 ms
Safe context
4K
Memory
325.4 GB / 288.0 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 33.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~32.5 GB host RAM) | 35.6 tok/s | 2962 ms | 4K |
| Coding | A | Very compromised (needs ~33.6 GB host RAM) | 35.3 tok/s | 5482 ms | 4K |
| Agentic Coding | A | Very compromised (needs ~35.9 GB host RAM) | 34.7 tok/s | 8122 ms | 4K |
| Reasoning | A | Very compromised (needs ~33.6 GB host RAM) | 35.3 tok/s | 6478 ms | 4K |
| RAG | A | Very compromised (needs ~35.9 GB host RAM) | 34.7 tok/s |
How Qwen3-Coder 480B A35B Instruct (480B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 187.2 GB | Low | S86 |
Q3_K_S | 3 | 235.2 GB | Low | F0 |
Copy-paste commands to run Qwen3-Coder 480B A35B Instruct on your machine.
Run
lms load Qwen3-Coder-480B-A35B-Instruct && lms server startYes, AMD Instinct MI350X 288GB can run Qwen3-Coder 480B A35B Instruct with a A grade (Very compromised (needs ~33.6 GB host RAM)). Expected decode speed: 35.3 tok/s.
Qwen3-Coder 480B A35B Instruct (480B parameters) requires approximately 325.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-Coder 480B A35B Instruct is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI350X 288GB, Qwen3-Coder 480B A35B Instruct achieves approximately 35.3 tokens per second decode speed with a time-to-first-token of 5482ms using Q4_K_M quantization.
For coding workloads, Qwen3-Coder 480B A35B Instruct on AMD Instinct MI350X 288GB receives a A grade with 35.3 tok/s and 4K context.
On AMD Instinct MI350X 288GB, Qwen3-Coder 480B A35B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, 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/qwen-3-coder-480b-a35b-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:
| 10152 ms |
| 4K |
NVFP4 |
| 4 |
268.8 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 292.8 GB | Medium | F0 |
Q5_K_M | 5 | 345.6 GB | High | F0 |
Q6_K | 6 | 393.6 GB | High | F0 |
Q8_0 | 8 | 513.6 GB | Very High | F0 |
F16 | 16 | 984.0 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.