Llama 4 Maverick 17B 128E needs ~254.2 GB but AMD Instinct MI210 64GB only has 64.0 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
190.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.2 tok/s
TTFT
87439 ms
Safe context
4K
Memory
254.2 GB / 64.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 254.2 GB, but this setup only exposes 64.0 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 | 2.2 tok/s | 47694 ms | 4K |
| Coding | F | Too heavy | 2.2 tok/s | 87439 ms | 4K |
| Agentic Coding | F | Too heavy | 2.2 tok/s | 127184 ms | 4K |
| Reasoning | F | Too heavy | 2.2 tok/s | 103337 ms | 4K |
| RAG | F | Too heavy | 2.2 tok/s | 158980 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 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 | 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 |
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 1623%.
~$20,000 MSRP
No, Llama 4 Maverick 17B 128E requires more memory than AMD Instinct MI210 64GB provides.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 254.2 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 MI210 64GB, Llama 4 Maverick 17B 128E achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 87439ms using Q4_K_M quantization.
For coding workloads, Llama 4 Maverick 17B 128E on AMD Instinct MI210 64GB receives a F grade with 2.2 tok/s and 4K context.
On AMD Instinct MI210 64GB, 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.
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.
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-mi210-64gb" 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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