Llama 4 Maverick 17B 128E needs ~260.6 GB but AMD Instinct MI300A 128GB only has 128.0 GB. Try a smaller quantization or lighter model.
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
132.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.3 tok/s
TTFT
23455 ms
Safe context
4K
Memory
260.6 GB / 128.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 260.6 GB, but this setup only exposes 128.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 8.4 tok/s | 12643 ms | 4K |
| Coding | F | Too heavy | 7.6 tok/s | 25331 ms | 4K |
| Agentic Coding | F | Too heavy | 8.1 tok/s | 34929 ms | 4K |
| Reasoning | F | Too heavy | 8.3 tok/s | 27720 ms | 4K |
| RAG | F | Too heavy | 8.1 tok/s | 43661 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on AMD Instinct MI300A 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 156.0 GB | Low | F0 |
Q3_K_S | 3 | 196.0 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 357%.
~$20,000 MSRP
No, Llama 4 Maverick 17B 128E requires more memory than AMD Instinct MI300A 128GB provides.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 260.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 4 Maverick 17B 128E is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI300A 128GB, Llama 4 Maverick 17B 128E achieves approximately 7.6 tokens per second decode speed with a time-to-first-token of 25331ms using Q4_K_M quantization.
For coding workloads, Llama 4 Maverick 17B 128E on AMD Instinct MI300A 128GB receives a F grade with 7.6 tok/s and 4K context.
On AMD Instinct MI300A 128GB, Llama 4 Maverick 17B 128E can safely use up to 4K tokens of context. The model's official context limit is 1.0M, 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-4-maverick-17b-128e-on-instinct-mi300a-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
224.0 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 244.0 GB | Medium | F0 |
Q5_K_M | 5 | 288.0 GB | High | F0 |
Q6_K | 6 | 328.0 GB | High | F0 |
Q8_0 | 8 | 428.0 GB | Very High | F0 |
F16 | 16 | 820.0 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
